System and method for managing routing of customer calls to agents

ABSTRACT

A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 16/773,805, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OFCUSTOMER CALLS TO AGENTS,” filed Jan. 27, 2020, which is a continuationof U.S. patent application Ser. No. 16/541,046, entitled “SYSTEM ANDMETHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Aug. 14,2019, which is a continuation of U.S. patent application Ser. No.16/276,165, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMERCALLS TO AGENTS,” filed Feb. 14, 2019, which is a continuation of U.S.patent application Ser. No. 16/110,940, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Aug. 23, 2018,which claims the benefit of U.S. Provisional App. No. 62/551,690, filedAug. 29, 2017, claims the benefit of U.S. Provisional App. No.62/648,325, filed Mar. 26, 2018, claims the benefit of U.S. ProvisionalApp. No. 62/648,330, filed Mar. 26, 2018, and claims the benefit of U.S.Provisional App. No. 62/687,130, filed Jun. 19, 2018, all of which areincorporated by reference in their entirety.

This application is also a continuation-in-part of U.S. patentapplication Ser. No. 16/791,750, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Feb. 14, 2020,which is a continuation of U.S. patent application Ser. No. 16/546,052,entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TOAGENTS,” filed Aug. 20, 2019, which is a continuation of U.S. patentapplication Ser. No. 16/283,378, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Feb. 22, 2019,which is a continuation of U.S. patent application Ser. No. 16/110,872,entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TOAGENTS,” filed Aug. 23, 2018, which claims the benefit of U.S.Provisional App. No. 62/551,690, filed Aug. 29, 2017, claims the benefitof U.S. Provisional App. No. 62/648,325, filed Mar. 26, 2018, claims thebenefit of U.S. Provisional App. No. 62/648,330, filed Mar. 26, 2018,and claims the benefit of U.S. Provisional App. No. 62/687,130, filedJun. 19, 2018, all of which are incorporated by reference in theirentirety.

This application is also a continuation-in-part of U.S. patentapplication Ser. No. 17/013,312, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Sep. 4, 2020, whichis a continuation of U.S. patent application Ser. No. 16/739,967,entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TOAGENTS,” filed Jan. 10, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/455,983, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Jun. 28, 2019,which is a continuation of U.S. patent application Ser. No. 16/267,331,entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMER CALLS TOAGENTS,” filed Feb. 4, 2019, which is a continuation of U.S. patentapplication Ser. No. 16/111,011, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Aug. 23, 2018,which claims the benefit of U.S. Provisional App. No. 62/551,690, filedAug. 29, 2017, claims the benefit of U.S. Provisional App. No.62/648,325, filed Mar. 26, 2018, claims the benefit of U.S. ProvisionalApp. No. 62/648,330, filed Mar. 26, 2018, and claims the benefit of U.S.Provisional App. No. 62/687,130, filed Jun. 19, 2018, all of which areincorporated by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to customer contact centers andtheir operation, and more particularly to a system and method formanaging routing of customer calls to agents.

BACKGROUND

Customer contact centers provide an important interface forcustomers/partners of an organization to contact the organization. Thecontact can be for a request for a product or service, for troublereporting, service request, etc. The contact mechanism in a conventionalcall center is via a telephone, but it could be via a number of otherelectronic channels, including e-mail, online chat, etc.

The contact center consists of a number of human agents, each assignedto a telecommunication device such as a phone or a computer forconducting email or Internet chat sessions that is connected to acentral switch. Using these devices, the agents generally provide sales,customer service, or technical support to the customers or prospectivecustomers of a contact center or of a contact center's clients.Conventionally, a contact center operation includes a switch system thatconnects callers to agents. In an inbound contact center, these switchesroute inbound callers to a particular agent in a contact center, or, ifmultiple contact centers are deployed, to a particular contact centerfor further routing. When a call is received at a contact center (whichcan be physically distributed, e.g., the agents may or may not be in asingle physical location), if a call is not answered immediately, theswitch will typically place the caller on hold and then route the callerto the next agent that becomes available. This is sometimes referred toas placing the caller in a call queue. In conventional methods ofrouting inbound callers to agents, high business value calls can besubjected to a long wait while the low business value calls are oftenanswered more promptly, possibly causing dissatisfaction on the part ofthe high business value caller.

In many call centers, the agents answering calls are organized into aplurality of groups or teams, with each group having primaryresponsibility of the calls in one or more call queues. Different agentgroups often have responsibility for different goals or functions of thecall center, such as generating customer leads, closing sales withprospects, and servicing existing customers. Routing an inbound callerto an appropriate group or team of the call center to address the needsof that caller can be a burdensome, time-consuming process.

There is a need for a system and method for identifying high businessvalue inbound callers at a call center during a time period in whichinbound callers are awaiting connection to an agent. Additionally, thereis a need to improve traditional methods of routing callers, such as“round-robin” caller routing, to improve allocation of limited callcenter resources to high business value inbound callers. Further, thereis a need to improve traditional methods of routing callers to a groupor team of agents appropriate to the caller's needs from a plurality ofagent groups that implement different functions or goals of the callcenter.

SUMMARY

Embodiments described herein can automatically route a call from acustomer to one of a plurality of call queue assignments based onpredicted value of the telephone call. Embodiments described herein canautomatically route a call from a customer to one of a plurality of callqueues associated with a plurality of groups of agents, based oninformation concerning an identified customer of an enterprise andpredicted value of the telephone call to that enterprise.

In various embodiments, the method retrieves from a customer database aset of enterprise customer data associated with an identified customerin a customer call. The customer database stores enterprise customerdata associated with prospects, leads, and purchasers of an enterprise,such as a sponsoring organization or client of the call center. Invarious embodiments, the enterprise customer data comprises one or moreof customer event data, activity event data, and attributions data.

In an embodiment, the process then retrieves customer demographic dataassociated with the identified customer. In various embodiments, thecustomer demographic data may be associated with the identified customerby two or more identifying data including name of the identifiedcustomer, address of the identified customer, and zip code of theidentified customer. In an embodiment, the customer management systemobtains a customer identifier for the received customer call from callerinformation associated with the inbound call.

Disclosed embodiments select a predictive model from a plurality ofpredictive models. Each of the plurality of predictive models isconfigured to determine a respective business outcome signalrepresentative of one or more of: likelihood of accepting an offer topurchase a product, likelihood of accepting an offer to purchase aproduct, likelihood of not lapsing in payments for a purchased product,and likelihood of accepting an offer to purchase a product and notlapsing in payments for the purchased products. The method and systemselects the one of the plurality of predictive models for which the setof enterprise customer data has a highest importance in determining therespective business outcome signal.

A method for managing routing of customer calls to agents executes aselected predictive model to determine a value prediction signal. Invarious embodiments, the value prediction signal includes one or more ofa first signal representative of the likelihood that the identifiedcustomer will accept an offer to purchase a product, a second signalrepresentative of the likelihood that the identified customer will lapsein payments for a purchased product, and a third signal representativeof the likelihood that the identified customer will accept an offer topurchase the product and will not lapse in payments for the purchasedproduct. In an embodiment, the selected predictive model determines thevalue prediction signal in real time by applying a logistic regressionmodel in conjunction with a tree-based model to the set of theenterprise customer data and the retrieved customer demographic data.

Based on the value prediction signal determined, the selected predictivemodel classifies the identified customer into one of a first value groupand a second value group. When the predictive model classifies theidentified customer into the first value group, the call managementsystem routes the customer call for the identified customer to a firstcall queue assignment. When the predictive model classifies theidentified customer into the second value group, the call managementsystem routes the customer call for the identified customer to a secondcall queue assignment. In an embodiment, the first call queue assignmentconnects the identified customer to one of a first pool of call centeragents who are authorized to present the offer to purchase a product,and the second call queue assignment connects the identified customer toone of a second pool of call center agents who are not authorized topresent the offer to purchase a product.

In an embodiment, the first call queue assignment comprises a firstqueue position in a call queue, and the second call queue assignmentcomprises a second queue position in a call queue. In an embodiment, thecall queue is a hold list for callers on hold for inbound customercalls. In an embodiment, the first call queue assignment includes afirst call queue for connection to an agent from a first pool of callcenter agents, and the second call queue assignment includes a secondcall queue for connection to an agent from a second pool of call centeragents. In another embodiment involving outbound customer calls (callbacks) initiated in response to inbound calls, the call queue is a callback list.

The selected predictive model can include a logistic regression modeland a tree-based model. In an embodiment, the logistic regression modelemploys l₁ regularization. In an embodiment, the logistic regressionmodel employs l₂ regularization. In an embodiment, the tree-based modelis a random forest ensemble learning method for classification. Thevalue prediction signal can include one or more of a first signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase a product, a second signal representative of alikelihood that the identified customer will lapse in payments for apurchased product, and a third signal representative of a likelihoodthat the identified customer will accept an offer to purchase theproduct and will not lapse in payments for the purchased product. Invarious embodiments, the value prediction signal is a buy-only signal, alapse-only signal, a buy-don't-lapse signal, or combination of thesesignals.

Disclosed embodiments can automatically route a call from a customer toone of a plurality of call queues associated with a plurality of groupsof agents, based on information concerning an identified customer andpredicted value of the telephone call. In a first step, the methodretrieves from a customer database a set of enterprise customer dataassociated with an identified customer in a customer call. The customerdatabase stores enterprise customer data associated with prospects,leads, and purchasers of an enterprise, such as a sponsoringorganization or client of the call center. In various embodiments, theenterprise customer data comprises one or more of customer event data,activity event data, and attributions data.

Various embodiments select a group of agents from a plurality of groupsof agents of the call center, wherein the selected group of agentsimplements a predetermined customer care interaction of the call center.The group of agents is selected based upon consistency of thepredetermined customer care interaction with the set of enterprisecustomer data for the identified customer.

In an embodiment of selecting a group of agents based upon consistencyof the predetermined customer care interaction with the set ofenterprise customer data, the predetermined customer care interaction iscustomer development of leads, and the set of enterprise customer dataincludes customer event data associated with a prospect. In anembodiment, the predetermined customer care interaction is cross-sellingof products, and the set of enterprise customer data includes customerevent data associated with a purchaser and activity event dataindicating likelihood of an additional purchase. In another embodiment,the predetermined customer care interaction is promoting a givenproduct, and the set of enterprise customer data includes activity eventdata indicating customer interest in a marketing campaign for the givenproduct. In a further embodiment, the predetermined customer careinteraction comprises closing product sales, and the set of enterprisecustomer data includes one or more of customer event data associatedwith a prospect, customer event data associated with a new businessapplicant, and attributions data associated with a prospect and a newbusiness applicant.

In an embodiment, a processor-based method for managing customer callswithin a call center comprises retrieving, by a processor, customerdemographic data associated with a customer identifier for an identifiedcustomer in a customer call; determining, by a predictive modelexecuting on the processor, a value prediction signal comprising one ormore of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product; wherein the predictive model comprises a logisticregression model operating in conjunction with a tree-based model;classifying, by the predictive model executing on the processor based onthe value prediction signal determined by the predictive model, theidentified customer into one of a first value group and a second valuegroup; and, in the event the classifying step classifies the identifiedcustomer into the first value group, routing, by the processor, theidentified customer to a first call queue for connection to one of afirst pool of call center agents who are authorized to present the offerto purchase the product; in the event the classifying step classifiesthe identified customer into the second value group, routing, by theprocessor, the identified customer to a second call queue for connectionto one of a second pool of call center agents who are not authorized topresent the offer to purchase the product.

In an embodiment, a processor-based method for managing customer callswithin a call center comprises, upon receiving a customer call at a callcenter from an identified customer, retrieving, by a processor, from acustomer database that stores enterprise customer data associated withcustomers of an enterprise, a set of the enterprise customer dataassociated with the identified customer in the customer call, whereinthe set of the enterprise customer data comprises one or more ofcustomer event data, activity event data, and attributions data;retrieving, by the processor, customer demographic data associated withthe identified customer in the customer call; selecting, by theprocessor, a predictive model from a plurality of predictive models,each of the plurality of predictive models being configured to determinea respective business outcome signal representative of one of more oflikelihood of accepting an offer to purchase a product, not lapsing inpayments for a purchased product, and accepting an offer to purchase aproduct and not lapsing in payments for the purchased products, whereinthe selected predictive model is the one of the plurality of predictivemodels for which the set of enterprise customer data has a highestimportance in determining the respective business outcome signal;executing, by the processor, the selected predictive model to generate avalue prediction signal by applying a logistic regression model inconjunction with a tree-based model to the set of the enterprisecustomer data and the retrieved customer demographic data, the valueprediction signal comprising one or more of a first signalrepresentative of the likelihood that the identified customer willaccept an offer to purchase a product, a second signal representative ofthe likelihood that the identified customer will lapse in payments for apurchased product, and a third signal representative of the likelihoodthat the identified customer will accept an offer to purchase theproduct and will not lapse in payments for the purchased product;classifying, by the selected predictive model executing on the processorbased on the value prediction signal determined by the selectedpredictive model, the identified customer into one of a first valuegroup and a second value group; and when the classifying step classifiesthe identified customer into the first value group, routing, by theprocessor, the customer call for the identified customer to a first callqueue assignment; wherein the first call queue assignment comprises oneor more of a first queue position in a call queue, and a first callqueue for connection to an agent from a first pool of call centeragents; and when the classifying step classifies the identified customerinto the second value group, routing, by the processor, the customercall for the identified customer to a second call queue assignment;wherein the second call queue assignment comprises one or more of asecond queue position in the call queue, and a second call queue forconnection to an agent from a second pool of call center agents.

In an embodiment, a system for managing customer calls within a callcenter comprises an inbound telephone call receiving device forreceiving a customer call to the call center; non-transitorymachine-readable memory that stores a customer database includingenterprise customer data associated with prospects, leads, andpurchasers of an enterprise serviced by the call center, wherein theenterprise customer data comprises customer event data, activity eventdata, and attributions data; a predictive modeling module that stores afirst predictive model of customer value, wherein the first predictivemodel comprises a first logistic regression model operating inconjunction with a first tree-based model configured to determine afirst business outcome signal, and that stores a second predictive modelof customer value; and that stores a second predictive model of customervalue, wherein the second predictive model comprises a second logisticregression model operating in conjunction with a second tree-based modelconfigured to determine a second business outcome signal; wherein eachof the first business outcome signal and the second business outcomesignal is representative of one or more of likelihood of accepting anoffer to purchase a product, likelihood of not lapsing in payments for apurchased product, and likelihood of accepting an offer to purchase aproduct and not lapsing in payments for the purchased products; and aprocessor, configured to execute an inbound queue management module,wherein the processor in communication with the non-transitorymachine-readable memory and the predictive modeling module executes aset of instructions instructing the processor to: upon receiving thecustomer call at the inbound telephone call receiving device from anidentified customer, retrieve from the customer database a set of theenterprise customer data associated with the identified customer in thecustomer call, wherein the set of enterprise customer data comprises oneor more of the customer event data, the activity event data, andattributions data; retrieve external third-party customer demographicdata associated with the identified customer; select one of the firstpredictive model of customer value or the second predictive model ofcustomer value, wherein for the selected predictive model the set ofenterprise customer data has a highest importance in determining therespective business outcome signal; determine a value prediction signalfor the identified customer via applying the selected predictive modelto the set of the enterprise customer data and the retrieved customerdemographic data; classify the identified customer into one of a firstvalue group and a second value group based on the value predictionsignal determined; and direct the inbound telephone call receivingdevice: when the inbound queue management module classifies theidentified customer into the first value group, to route the customercall of the identified customer to a first call queue assignment;wherein the first call queue assignment comprises one or more of a firstqueue position in a call queue, and a first call queue for connection toan agent from a first pool of call center agents; and when the inboundqueue management module classifies the identified customer into thesecond value group, to route the customer call of the identifiedcustomer to a second call queue assignment; wherein the second callqueue assignment comprises one or more of a second queue position in thecall queue, and a second call queue for connection to an agent from asecond pool of call center agents.

In an embodiment, a processor-based method for managing customer callswithin a call center comprises, upon receiving a customer call at a callcenter from an identified customer, retrieving, by a processor, from acustomer database that stores enterprise customer data associated withcustomers of an enterprise, a set of the enterprise customer dataassociated with the identified customer in the customer call, whereinthe set of the enterprise customer data comprises one or more ofcustomer event data, activity event data, and attributions data;retrieving, by the processor, customer demographic data associated withthe identified customer in the customer call; selecting, by theprocessor, a predictive model from a plurality of predictive models;each of the plurality of predictive models being configured to determinea respective business outcome signal representative of one of more oflikelihood of accepting an offer to purchase a product, likelihood ofnot lapsing in payments for a purchased product, and likelihood ofaccepting an offer to purchase a product, and not lapsing in paymentsfor the purchased products; wherein the selected predictive model is theone of the plurality of predictive models for which the set ofenterprise customer data has a highest importance in determining therespective business outcome signal; executing, by the processor, theselected predictive model to generate a value prediction signal byapplying a logistic regression model in conjunction with a tree-basedmodel to the set of the enterprise customer data and the retrievedcustomer demographic data, the value prediction signal comprising one ormore of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product; classifying, by the selected predictive modelexecuting on the processor based on the value prediction signaldetermined by the selected predictive model, the identified customerinto one of a first value group and a second value group; and when theclassifying step classifies the identified customer into the first valuegroup, routing, by the processor, the customer call for the identifiedcustomer to a priority call queue assignment; wherein the priority callqueue assignment comprises one or more of a priority queue position in acall queue, and a priority call queue for connection to an agent from afirst pool of call center agents; and when the classifying stepclassifies the identified customer into the second value group, routing,by the processor, the customer call for the identified customer to asubordinate call queue assignment; wherein the subordinate call queueassignment comprises one or more of a subordinate queue position in thecall queue, and a subordinate call queue for connection to an agent froma second pool of call center agents.

In an embodiment, a computer-implemented method for managing customercalls within a call center comprises retrieving, by a computer, customerdata associated with a customer in a call session; executing, by thecomputer, a predictive machine-learning model configured to determine avalue prediction signal representative of a likelihood of achieving acustomer transaction by applying the predictive machine-learning modelto the customer data, the predictive machine-learning model classifyingthe customer into a first value group or into a second value group,routing, by the computer, the customer to a first call queue in theevent the customer is classified into the first value group; androuting, by the computer, the customer to a second call queue in theevent the customer is classified into the second value group.

In an embodiment, a computer-implemented method for managing customercalls within a call center comprises retrieving, by a computer, customerdata associated with an identified customer in a call session;executing, by the computer, a predictive machine-learning modelconfigured to determine a value prediction signal representative of alikelihood of achieving a predetermined customer care interaction byapplying the predictive machine-learning model to the customer data, thepredictive machine-learning model classifying the identified customerinto a first value group or into a second value group, routing, by thecomputer, the identified customer to a first call queue in the event theidentified customer is classified into the first value group; androuting, by the computer, the identified customer to a second call queuein the event the identified customer is classified into the second valuegroup.

In an embodiment, a system for managing customer calls within a callcenter comprises an inbound telephone call-receiving device forreceiving a customer call to the call center and for associating thecustomer call with an identified customer; non-transitory,machine-readable memory that stores customer data; and a computerconfigured to execute a predictive modeling module, wherein the computerin communication with the non-transitory machine-readable memoryexecutes a set of instructions instructing the computer to: in responseto receiving the customer call, retrieve from the non-transitory,machine-readable memory a set of customer data associated with theidentified customer; determine a value prediction signal representativeof a likelihood of achieving a customer transaction by applying thepredictive machine-learning model to the retrieved customer data;classify the identified customer into one of a first value group and asecond value group based on the value prediction signal determined; anddirect the inbound call receiving device: to route the identifiedcustomer to a first call queue in the event the computer classifies theidentified customer into the first value group; and to route theidentified customer to a second call queue in the event the computerclassifies the identified customer into the second value group.

Other objects, features, and advantages of the present disclosure willbecome apparent with reference to the drawings and detailed descriptionof the illustrative embodiments that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting embodiments of the present disclosure are described by wayof example with reference to the accompanying figures which areschematic and are not intended to be drawn to scale. Unless indicated asrepresenting the background art, the figures represent aspects of thedisclosure.

FIG. 1 is a system architecture for a customer management system of aninbound contact center, in accordance with an embodiment.

FIG. 2 illustrates a method for routing a customer call to an agent, inaccordance with an embodiment.

FIG. 3 illustrates a queue arrangement for routing prospective customersto agents of a call center, in accordance with an embodiment.

FIG. 4 is a system architecture for a customer management system of acontact center, in accordance with an embodiment.

FIG. 5 is a system architecture for an answering queues routingsubsystem of a customer management system of an inbound contact center,in accordance with an embodiment.

FIG. 6 is a system architecture for a call back queue routing subsystemof a customer management system of an inbound contact center, inaccordance with an embodiment.

FIG. 7 illustrates a method for routing a customer call to an agent, inaccordance with an embodiment.

FIG. 8 is a system architecture for a customer management system of acontact center, in accordance with an embodiment.

FIG. 9 illustrates a method for routing a customer call to an agent, inaccordance with an embodiment.

FIG. 10 is an architecture for a customer database including data storesfor four target groups for marketing and customer acquisition of theenterprise, in accordance with an embodiment.

FIG. 11 is a flow chart diagram of attribution processes for trackingpersons across events between customer groups (prospects, leads, newbusiness applicants, and sales), in accordance with an embodiment.

FIG. 12 is a schematic diagram of customer database event tables forcustomer groups' prospect, lead, new business, and sale, and of tablesfor attribution between events, in accordance with an embodiment.

FIG. 13 is a graph of a receiver operator curve (ROC) for a valueprediction model, in accordance with an embodiment.

FIG. 14 is a graph of a receiver operator curve (ROC) for a valueprediction model, in accordance with an embodiment.

FIG. 15 is a graph of lift across deciles of model scores for a valueprediction model, in accordance with an embodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which depict non-limiting, illustrativeembodiments of the present disclosure. Other embodiments may be utilizedand logical variations, e.g., structural and/or mechanical, may beimplemented without departing from the scope of the present disclosure.To avoid unnecessary detail, certain information, items, or detailsknown to those skilled in the art may be omitted from the following.

Contact routing at an inbound contact center can be structured innumerous ways. An individual employed by the contact center to interactwith callers is referred to in the present disclosure as an “agent.”Contact routing can be structured to connect callers to agents that havebeen idle for the longest period of time. In the case of an inboundcaller where only one agent may be available, that agent is generallyselected for the caller without further analysis. In another example ofrouting an inbound call, if there are eight agents at a contact center,and seven are occupied with callers, the switch will generally route theinbound caller to the one agent that is available. If all eight agentsare occupied with contacts, the switch will typically put the caller onhold and then route the caller to the next agent that becomes available.More generally, the contact center will set up a queue of inboundcallers and preferentially route the longest-waiting callers to theagents that become available over time. A pattern of routing callers toeither the first available agent or the longest-waiting agent issometimes referred to as “round-robin” caller routing.

In general, when a caller is placed in a call queue, the caller's queueposition is dependent upon the receipt time of the call at the vendorlocation. No consideration is given to the identity of the caller or thepotential value of the call. While this is a democratic way to handleinbound calls, it may not be good for business. For instance, a largenumber of low business value calls may be in a queue when a highbusiness value call is received. As a result, the high business valuecall is subjected to a long wait while the low business value calls areanswered—with attendant dissatisfaction on the part of the high businessvalue caller. When call centers have an inadequate number of skilledagents to handle all callers, such as at times of peak call volume,challenges of effectively handling high-value callers can be especiallysevere. The method and system of the present disclosure are intended toalleviate these problems.

Call center operations include various types of call queues. Forexample, an inbound caller may be put on hold and placed on an answeringqueue or hold list, to be routed to a live agent when the caller hasmoved up to the first position in the call queue. In another example, ifcall centers are unable to route inbound callers to a live agent withina reasonable period of time, an inbound caller may be placed on a callback list, to receive a call back from a live agent when the caller hasmoved up to the first position on the call back list. In someembodiments, an inbound caller is assigned to one of a plurality of callqueues (e.g., two queues) respectively associated with a plurality ofpools of agents. In an illustrative embodiment, a first inbound calleris assigned to a first call queue to be routed to live agent in a firstpool of agents who are skilled in sales and who are authorized to closea sale with the first caller, while a second inbound caller is assignedto a second call queue to be routed to live agent in a second pool ofagents who are unskilled in sales and who are not authorized to close asale with the second caller. As used in the present application, a “callqueue assignment” means assignment of an inbound call from an identifiedcustomer to a call queue for connection to a call center agent. Invarious embodiments, the call queue assignment may include a given queueposition and/or may include assignment to a call queue selected from aplurality of call queues.

Methods and systems described herein can automatically assign an inboundcall from a customer to a call queue assignment based on predicted valueof the inbound telephone call. The present system and method identifiesan inbound caller with data from an internal customer database of anenterprise, e.g., sponsoring organization or client of the contactcenter. Upon identifying the customer, the process retrieves customerdemographic data associated with the identified customer. The methodselects a selected predictive model from a plurality of predictivemodels based upon enterprise customer data stored by the customerdatabase for the identified customer. The selected predictive modeldetermines a value prediction signal for the identified customer. Basedon the value prediction signal determined, the predictive modelclassifies the identified customer into one of a first value group and asecond value group. When the predictive model classifies the identifiedcustomer into the first value group, the call management system assignsthe call received from identified customer to a first call queueassignment. When the predictive model classifies the identified customerto the second value group, the call management system assigns the callreceived from the identified customer to a second call queue assignment.

In an embodiment, the first value group comprises customers having afirst set of modeled values, and the second value group comprisescustomers having a second set of modeled values, wherein modeled valuesin the first set of modeled values are higher than modeled values in thesecond set of modeled values. In an embodiment, the modeled values aremodeled lifetime values. In an embodiment, when the predictive modelclassifies the identified customer into the first value group havinghigher modeled values, the call management system assigns the identifiedcustomer to a priority queue assignment; when the predictive modelclassifies the identified customer into the second value group havinglower modeled values the call management system assigns the identifiedcustomer to a subordinate queue assignment. As used in the presentdisclosure, a priority queue assignment and subordinate queue assignmentare relative terms, in which a priority queue assignment is morefavorable than a subordinate queue assignment. In an embodiment, apriority queue assignment is a more advanced position within a givencall queue than a subordinate queue assignment. In another embodiment, apriority queue assignment is an assignment to a more favorable callqueue among a plurality of call queues (e.g., a queue for liveconnection to an agent from a pool of skilled agents) than a subordinatequeue assignment (e.g., a queue for live connection to an agent from apool of unskilled agents).

In other embodiments disclosed herein, an inbound caller is routed toone of a plurality of groups of call center agents (e.g., two groups),respectively associated with a plurality of call queues. Each group ofagents is assigned to implement one or more predetermined function orgoal of the call center; in the present disclosure a given goal orfunction of the call center is called a “customer care interaction.”Customer care interactions may be broad in scope, such as generatingcustomer leads, closing sales of products of a sponsoring enterprise ofthe call center, and customer service interactions with existingcustomers or purchasers (individuals who previously purchased a productof the sponsoring enterprise). Customer care interactions also can bemore specific in scope, such as promoting a given product as part of amarketing campaign. The systems and methods of the present disclosureimprove the routing of inbound calls of identified customers to groupsof agents that handle customer care interactions appropriate to thecallers.

Embodiments described herein can automatically assign an inbound callfrom a customer to a call queue position for connection to one of aplurality of groups of call center agents, wherein the call queueposition is based on predicted value of the inbound telephone call. Uponreceiving an inbound call, a call management system retrieves a set ofenterprise customer data associated with an identified customer from acustomer database. The system retrieves customer demographic dataassociated with the identified customer. In an embodiment, the systemselects a group of agents from a plurality of groups of agents of thecall center, wherein the selected group of agents implements apredetermined customer care interaction of the call center. The group ofagents is selected based upon consistency of the predetermined customercare interaction with the set of enterprise customer data for theidentified customer.

The customer database tracks individuals who are customers of asponsoring organization or client of the call center, or otherenterprise served by the call center, associating these individuals withone or more groups representing general types of customers. In anembodiment, these customer groups include prospects, leads, newbusiness, and purchasers (also herein called sales). The customerdatabase joins these four groups to better evaluate marketing activitiesand customer service activities of the call center. Data from thecustomer database is used in building stronger predictive models usedfor routing inbound calls; in the present disclosure, customer databasedata is sometimes called “enterprise customer data,” denoting datarelating to customers of the sponsoring enterprise. Enterprise customerdata retrieved for an identified customer is used in selecting asuitable predictive model from a plurality of predictive models, basedupon consistency of the selected predictive model as a modeling targetwith the set of enterprise customer data for the identified customer.Enterprise customer data is associated with one or more enterprisecustomer record identifying a given customer tracked by the customerdata. Enterprise customer data includes data identified with one or morecustomer groups (also herein called customer event data), activity eventdata, and attributions data, among other types of data.

Methods and systems described herein can employ a pre-sale predictivemodel relating to offer for sale of one or more product offered orsupplied by a sponsoring organization of an inbound contact center. Invarious embodiments, the products offered or supplied by a sponsoringorganization require payments by the customer for a period followingclosing the sale, such as premiums to maintain in force an insurancepolicy or other financial product, or installment plans for productpurchase. In various embodiments, the pre-sale predictive modelincorporates information on a minimum period of time of customerpayments required to achieve a beneficial transaction for the sponsoringorganization, wherein failure of the customer to make payments over atleast this minimum time period is sometimes referred to herein as“lapse.” The presale predictive model forecasts customer behavior toimprove the probability of closing a sale of an offered product to aninbound customer, and to reduce the probability that the customer willlapse in payment for the purchased product.

The predictive model can classify inbound callers into two or moregroups. In an embodiment, two value groups are modeled to model higherpredicted value and lower predicted value, respectively, to thesponsoring organization. In various embodiments, this classificationgoverns value-based routing of inbound telephone calls for response byagents to allocate limited resources of the inbound contact center. Anindividual employed by the contact center to interact with callers isreferred to herein as an “agent.”

The inbound contact center is sometimes called simply a contact centeror a call center. The individuals that interact with the contact centerusing a telecommunication device are referred to herein as callers, andalternatively are referred to as inbound callers, as customers, or asany of the general types of customer. As used in the present disclosure,a “customer” may be an existing customer or a prospective customer ofthe sponsoring organization, including any of the general groups ofcustomer tracked in the customer database. In an embodiment, a customeris associated with the one or more of the groups: prospects, leads, newbusiness, and sales (also herein called purchasers). A given individualmay be associated with multiple such groups over different stages ofcustomer acquisition. For example, a purchaser may have previously beenone or more of a prospect, a lead, or a new business applicant.

In an embodiment of the customer groups in the customer database,“prospects” are individuals that have contacted the enterprise. Inboundprospects may or may not be customers in the customer databases. In anembodiment, if an inbound caller is not identified with an individual inthe customer database, the database opens a new record for that callerin the prospects group. “Leads” are individuals who have expressedinterest in one or more products of the enterprise; as used herein,products may include goods or services sold by the enterprise, or acombination of these. A lead may have previously been a prospect, or maynot have been a prospect (e.g., an individual that searches for productsor services of the enterprise online). “New Business” (also hereincalled new business applicants) identifies applicants to purchase one ormore product of the enterprise, where such purchase requiresunderwriting. These applicants may have been prospects, leads, or both.“Purchasers” (also herein called “sales”) generally are individuals thatown a product of the enterprise. Purchasers may have been prospects,leads, new business applicants, or any combination of these groups.

Methods and systems described herein can employ a predictive modelrelating to offering for sale one or more products offered or suppliedby a sponsoring organization of an inbound contact center. In variousembodiments, the products offered or supplied by a sponsoringorganization require payments by the customer for a period followingclosing the sale, such as premiums to maintain in force an insurancepolicy or other financial product, or installment plans for productpurchase. A pre-sale prediction model can incorporate information on aminimum period of time of customer payments required to achieve abeneficial transaction for the sponsoring organization and uses thisinformation in determining conditions for “lapse.” The pre-salepredictive model forecasts customer behavior to improve the probabilityof closing a sale of an offered product to an inbound customer, and toreduce the probability that the customer will lapse in payment for thepurchased product.

The predictive model can classify inbound callers into two or more valuegroups. In an embodiment, two value groups are modeled to model higherpredicted value and lower predicted value, respectively, to thesponsoring organization. In various embodiments, this classificationgoverns value-based routing of inbound telephone calls for response byagents, to allocate limited resources of the inbound contact center. Anindividual employed by the contact center to interact with callers issometimes referred to herein as an “agent.”

A key metric for value-based classification of a customer who haspurchased a product is called a “lifetime value” of the product sale tothat customer. In various embodiments, lifetime value includes the sumof all associated costs over product lifetime, netted against revenuefor the product sale. The lifetime value for the product (insurancepolicy) sold to that customer is the net value of all premiums paid,over the sum of all such associated costs during that policy life.

In an illustrative embodiment involving sale of an insurance policy,associated costs over product lifetime include various sales acquisitioncosts, including marketing costs distributed across inbound calls, costof operating the inbound contact center distributed across inboundcalls, and commission at the time of sale. In this example, additionalassociated costs include cost of providing the insurance policy, andclaims or death benefit. In various embodiments, total costs for acustomer are modeled based on the customer's age, gender, policy faceamount, and whether the policy is lapsed, and by applying formulas basedon amortization of total marketing costs and operations costs. In anillustrative embodiment involving sale of an insurance policy, totalrevenue for a customer is modeled based on the customer's age, gender,policy face amount, and whether the policy is lapsed (if so, when). Themodel calculates expected total premium payments based on age and gendervia lookup of mortality statistics.

Methods and systems described herein can identify lapse (e.g., for agiven product or class of products) with a pre-determined period of timefollowing sale of the product and define lapse as failure of thecustomer to make payments for the product over at least this period oftime. In various embodiments, this predetermined period of time is basedupon modeling a minimum period of time for achieving a positive lifetimevalue for the product sale. This model compares total payments receivedwith associated costs over different product lifetimes to determine thepredetermined period. In one embodiment, product lifetime represents aperiod of time over in which the customer has continued to make purchasepayments for the product, such as premiums or installment payments. Inanother embodiment, lifetime value is measured during the full term orlife of an insurance policy or other financial instrument until allclaims and death benefits have been paid, even if all premiums or othercustomer payments had been paid prior to this time.

FIG. 1 shows a system architecture for a customer management system 100,according to an illustrative embodiment. In the present disclosure, thecustomer management system 100 is sometimes called an inbound callcenter or inbound contact center, referring to its primary function ofreceiving inbound customer calls. Customer management system 100includes an inbound queue management system 102, also called an inboundcall management system. Inbound queue management system 102 managesassignment of inbound telephone calls for response by agents to multiplequeues (e.g., two queues) based on predicted value of the inboundtelephone call. Inbound queue management system 102 includes ananalytical engine 104 containing a call evaluation sub-module 108, and apredictive modeling module 110 including a regression model 114 and atree-based model 118.

Inbound call management system 102 is interfaced with one or moreinternal databases 120 of the inbound contact center, such as callhistory database 124 and account information database 128. In anembodiment, analytical engine 104 interacts with external services,applications, and databases, such as third party databases 130, throughone or more application programmable interfaces, an RSS feed, or someother structured format, via communication network 135. In theembodiment of FIG. 1, inbound queue management system 102 retrieves datafrom one or more third party databases 130, including a consumerdemographic database 132 and a directory service database 134.

Predictive modeling module 110 builds one or more models that modelbehaviors of customers such as likelihood that a caller will purchase aproduct offered by the call center, and likelihood that the caller willlapse in payments for a purchased product. The predictive modelingmodule analyzes each inbound customer call using data associated with acustomer identifier for the inbound caller. This customer identifier maybe obtained from various sources by the call evaluation sub-module 108.Input data used in predictive modeling includes data retrieved from oneor more of internal databases 120, and third party databases 130. Thisinput data also may include data derived from the retrieved data thathas been transformed by analytical engine 104 in order to facilitatepredictive modeling, as described herein.

Databases 120 are organized collections of data, stored innon-transitory machine-readable storage. In an embodiment, the databasesmay execute or may be managed by database management systems (DBMS),which may be computer software applications that interact with users,other applications, and the database itself, to capture (e.g., storedata, update data) and analyze data (e.g., query data, execute dataanalysis algorithms). In some cases, the DBMS may execute or facilitatethe definition, creation, querying, updating, and/or administration ofdatabases. The databases may conform to a well-known structuralrepresentational model, such as relational databases, object-orienteddatabases, and network databases. Database management systems mayinclude MySQL, PostgreSQL, SQLite, Microsoft SQL Server, MicrosoftAccess, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, orFileMaker Pro.

Analytical engine 104 can be executed by a server, one or more servercomputers, authorized client computing devices, smartphones, desktopcomputers, laptop computers, tablet computers, PDAs and other types ofprocessor-controlled devices that receive, process, and/or transmitdigital data. Analytical engine 104 can be implemented using asingle-processor system including one processor, or a multi-processorsystem including any number of suitable processors that may be employedto provide for parallel and/or sequential execution of one or moreportions of the techniques described herein. Analytical engine 104performs these operations as a result of central processing unitexecuting software instructions contained within a computer-readablemedium, such as within memory. In one embodiment, the softwareinstructions of the system are read into memory associated with theanalytical engine 104 from another memory location, such as from astorage device or from another computing device via communicationinterface. In this embodiment, the software instructions containedwithin memory instruct the analytical engine 104 to perform processesdescribed below. Alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to implement theprocesses described herein. Thus, implementations described herein arenot limited to any specific combinations of hardware circuitry andsoftware.

Predictive modeling module 110 generates a value prediction signalrepresentative of one or more of the following customer behaviors: (a)likelihood that the customer will accept an offer to purchase a product,(b) likelihood that the customer will lapse in payments for a purchasedproduct, and (c) likelihood that the customer will accept an offer topurchase the product and will not lapse in payments for the purchasedproduct. In certain embodiments, the predictive modeling module canpredict more than one of these customer behaviors. For example, thepredictive model may first determine the customer behavior (a)likelihood that the customer will accept an offer to purchase a product,followed by determining the customer behavior (b) likelihood that thecustomer will lapse in payments for a purchased product, in order todetermine a value prediction signal. Based on this value predictionsignal, the analytical module, in conjunction with the predictivemodeling module, classifies each customer call into one of two, or more,value groups. Depending on the value group determined for each customercall, analytical engine 104 directs routing of the customer call to oneof two or more answering queues 150 to await connection to an agent ofthe call center. In FIG. 1, two call queues 154 and 158 are shown.Value-based classification of inbound calls by inbound call managementsystem 102 represents a significant improvement over traditional methodsof routing callers, such as “round-robin” caller routing.

Inbound call management system 102 interfaces with an inbound telephonecall receiving system 140. In customer management system 100, inboundcall management system 102 and call receiving system 140 may beintegrated in a single computing platform. Alternatively these systemsmay be based on separate computing platforms. In certain embodiments,the computing platform(s) are interfaced with computer-telephoneintegration (“CTI”) middleware. In an embodiment, inbound telephone callreceiving system 140 includes a telephony device that accepts inboundtelephone calls through a telephony interface 141, such as conventionalT1 or fiber interfaces. Inbound telephone call receiving system 140accepts inbound telephone calls through interface 141 and obtains callerinformation associated with the inbound calls, such as Automatic NumberIdentification (“ANI”) and Dialed Number Identification Service (“DNIS”)information 145. ANI is a signaling system feature in which a series ofdigits, either analog or digital, are included in the call identifyingthe source telephone number of the calling device. DNIS is a telephonefunction that sends the dialed telephone number to an answering service.The DNIS need not be a telephone number associated with any physicallocation.

Inbound telephone call receiving system 140 may include an AutomaticCall Distributor (“ACD”) system 142; a Voice Response Unit (“VRU”)system 144; a private branch exchange (“PBX”) switch 146; a Voice overInternet Protocol (“VOIP”) server 148; or any combination of suchdevices. In an embodiment, intrasite telephony access within the callcenter may be managed by a private branch exchange (PBX) switch 146. Inan embodiment, PBX switch 146 operates in coordination with ACD 142 todistribute inbound calls customer service stations locally networkedcall center agents, such agents in first pool 160 and second pool 170.In further embodiments, inbound inquiries may include e-mail or instantmessages that provide inquiry information based on login ID, e-mailaddress, IP, or instant message address. In such an embodiment, the callcenter can gather additional information by an automated e-mail orinstant message survey response, which can be used to request varioustypes of customer identifier data.

An identified customer can be an inbound caller for which the customermanagement system 100 has obtained reliable identifying data. This datais used by inbound queue management system 102 to retrieve or identifydata associated with that customer. In an embodiment, an identifiedcustomer is a customer for which the system 100 has reliably identifiedat least two of name, address, and zip code. In an embodiment, VoiceResponse Unit (“VRU”) system 144 collects customer identifier data, suchas name, address, and zip code, through automated interaction with thecustomer. In the present disclosure, this VRU data collection issometimes called a telegreeter. For instance, VRU 144 may query aninbound caller to collect customer identifier information when ANI isnot operative, e.g., when caller-ID is blocked. In an embodiment,inbound call management system 102 communicates with a third partydirectory service 134. Directory service 134 can provide additionalcaller identification information, such as name and address information,for inbound callers that are initially identified only by a telephonenumber. For an inbound caller that is a new lead of the enterprise, thisadditional caller identification information can be stored in AccountInfo database 128 as profile data for that lead.

Inbound telephone calls received through interface 141 are distributedto answering queues 150 for response by agents operating telephonydevices. In the embodiment of FIG. 1, answering queues 150 include afirst call queue 154 for response by one of the agents in a first pool160 of call center agents, and a second call queue 158 for response byone of the agents in a second pool of agents 170. Although FIG. 1depicts an embodiment of the present invention that orders inboundtelephone calls, alternative embodiments apply inbound queue managementto schedule other types of inbound inquiries, such as e-mail or instantmessage inquiries. In an embodiment, call center agents in agent pools160 and 170 are groups of customer service representatives or agentsdeployed at workstations or agent devices communicatively coupled tocall management system 100.

The agents are associated with a sponsoring organization that sells orsupplies products with the assistance of the call center. In anembodiment, the organization generates sales of one or more productthrough advertisements that give a phone number to prospectivecustomers, and the prospective customers call into the call center usingthis phone number. In various embodiments, the agents in first pool 160are authorized to offer an advertised product to a prospective customer(inbound caller), while the agents in second pool 170 are not authorizedto offer the advertised product. In the present disclosure, for an agentto be authorized to offer a product to a prospective customer or leadmeans that the agent is authorized to pursue a sale of the product tothe lead.

A sponsoring organization for customer management system 100 is aninsurance company or other financial services company, and the agentsmay include insurance agents. In some cases, an insurance agent may beassociated with only a single insurance provider (sometimes referred toas a “captive” insurance agent). In other cases, an “independent”insurance agent may be associated with several different insuranceproviders. In an embodiment, the agents in the first pool 160 arelicensed to sell insurance. In some cases, the producers may be licensedto sell different types of insurance products, might have differentareas of expertise, needs, etc. In some embodiments, agents in the firstpool 160 are selected for performance metrics related to sales. Agentsales performance may be measured by aggregate sales productivitymetrics, as well as distributed performance metrics such as salesmetrics by product types, etc.

While the agents in second pool 170 are not authorized to offer theproduct(s) to the inbound caller (prospective customer or lead), theseagents perform an important role in lead nurturing. Forwarding aninbound inquiry to a live agent with little or no wait time, sometimesreferred to herein as a “warm transfer,” has been observed tosignificantly increase probability of a successful sale to that customerin a later interaction. In some embodiments, agents in the second pool170 are selected for skills related to agent-customer communications,which can be measured in indicators of customer satisfaction such asfeedback on customer experiences.

FIG. 2 is a flowchart of a call management process 200 for automaticallyrouting an inbound call from a customer to one of a plurality of callqueue assignments based on predicted value of the inbound telephonecall. The method retrieves from a customer database a set of enterprisecustomer data associated with an identified customer in a customer call.The customer database stores enterprise customer data associated withprospects, leads, and purchasers of an enterprise. In certainembodiments, the customer database also stores enterprise customer dataassociated with new business applicants of the customer enterprise. Theprocess then retrieves customer demographic data associated with theidentified customer.

The method 200 selects a predictive model from a plurality of predictivemodel, by selecting a predictive model for which the set of enterprisecustomer data has the highest expected impact on likelihood of arespective business outcome associated with that predictive model. Theselected predictive model, including a logistic regression modeloperating in conjunction with a tree-based model, determines a valueprediction signal for the identified customer. Based on the valueprediction signal determined, the predictive model classifies theidentified customer into one of a first value group and a second valuegroup. When the predictive model classifies the identified customer intothe first value group, the call management system routes the callreceived from identified customer to a first call queue assignment,i.e., first queue position and/or first call queue. When the predictivemodel classifies the identified customer into the second value group,the call management system routes the call received from identifiedcustomer to a second call queue assignment, i.e., second queue positionand/or second call queue.

The steps of process 200 may be performed by one or more computingdevices or processors in the customer management system of FIG. 1. In anembodiment, the plurality of steps included in process 200 may beperformed by an inbound queue management system 102 of a customermanagement system 100 of a call center, in operative communication withan inbound call receiving system 140 that receives a customer call ofthe identified customer.

The call management process 200 is initiated at step 202 in response tothe customer management system 100 receiving an incoming customer call,by retrieving from a customer database enterprise customer dataassociated with an identified customer in the customer call. Thecustomer database stores enterprise customer data associated withprospects, leads, and purchasers of an enterprise. In variousembodiments, the enterprise customer data used in selecting the selectedpredictive model comprises one or more of customer event data, activityevent data, and attributions data. In certain embodiments, the customerdatabase also stores enterprise customer data associated with newbusiness applicants of the enterprise.

At step 204, the call management process retrieves customer demographicdata associated with the identified customer. In an embodiment, theprocess retrieves the customer demographic data from a third partydatabase. In an embodiment, the third party customer demographic data isassociated with identified customer by matching the data to one or moreenterprise customer identifier stored by the customer database. In anembodiment, the call management process builds a simplified data filefrom the set of enterprise customer data and the retrieved third partydemographic data as input to further steps of the process.

At step 206, the call management process selects a predictive model froma plurality of predictive models stored by the customer managementsystem. Each of the plurality of predictive model is configured topredict likelihood of a respective business outcome comprising one ormore of likelihood and accepting an offer to purchase a product,likelihood of not lapsing in payments for a purchased product, andlikelihood of accepting an offer to purchase a product and not lapsingin payments for the purchased products. Step 206 selects the one of theplurality of predictive models for which the set of enterprise customerdata has the highest expected impact on likelihood of the respectivebusiness outcome. In an embodiment, the predictive model is selectedbased on highest expected likelihood of achieving one or more businessoutcome associated with that predictive model. In various embodiments ofstep 206, the predictive model is selected based upon enterprisecustomer data for the inbound caller including one or more of customerevent data, activity event data, and attributions data.

In an embodiment of step 206, the respective business outcome comprisesone or more of the following general buy/lapse behaviors: accepting anoffer to purchase a product, not lapsing in payments for a purchasedproduct, and accepting an offer to purchase a product and not lapsing inpayments for the purchased products. In an embodiment, the respectivebusiness outcome comprises a more specific business outcome, such as abusiness outcome representing an instance of one of these generalbuy/lapses behaviors. In an illustrative embodiment, the respectivebusiness outcome is likelihood of purchase of a given product of theenterprise. In another illustrative embodiment, the respective businessoutcome is likelihood of lapse, targeted to new business applicants ofthe enterprise.

In an embodiment of step 206, the respective business outcome of theselected business model targets one or more of the prospects, leads, andpurchasers of the enterprise, and the enterprise customer data comprisescustomer event data associated with the one or more prospects, leads,and/or purchasers of the enterprise targeted by the respective businessoutcome. In an example, based on enterprise customer data associatedwith a prospect, step 206 selects a predictive model targeted atprospects in which the respective business outcome includes likelihoodof generating customer leads. In another example, based upon enterprisecustomer data associated with a purchaser, step 206 selects a predictivemodel targeted at purchasers for which the respective business outcomeincludes likelihood of cross selling an additional product to thepurchaser.

In another embodiment of step 206, the set of enterprise customer dataincludes activity event data, and the predictive model is selected basedupon the one of the plurality of predictive models for which theactivity event data has the highest expected impact on the likelihood ofthe respective business outcome. In an example, the predictive model isselected based on enterprise customer data for a sale of an insuranceproduct, including activity event data indicating failure to pay firstyear premiums. The selected predictive model models the likelihood thatthe identified customer will lapse in payments for a purchased product.

At step 208, the call management process executes the selectedpredictive model to determine a value prediction signal. In anembodiment, the selected predictive model applies a logistic regressionmodel in conjunction with a tree-based model to the set of theenterprise customer data and the retrieved customer demographic data. Inan embodiment, the call management process executes the selectedpredictive model to determine the value prediction signal in real time.By determining the value prediction signal in real time, the presentmethod automatically routes incoming customer calls to a call queueassignment expeditiously.

In various embodiments of step 208, the value prediction signal includesone or more of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product.

In various embodiments of step 208, the value prediction signaldetermines a likelihood that the identified customer will lapse inpayments for a purchased product by determining a likelihood that theidentified customer will fail to make a payment for the purchasedproduct during a predetermined time period following purchase of thepurchased product. In an embodiment, the predetermined time period waspreviously determined by modeling lifetime value over varying durationsof the time period following purchase of the purchased product.

In various embodiments of step 208, the regression model employs l₁regularization. In various embodiments, the logistic regression modelemploys l₂ regularization. In various embodiments, the tree-based modelis a random forests ensemble learning method for classification.

At step 210, the call management process classifies the customer callfor the identified customer into either a first value group or a secondvalue group, based on the value prediction signal determined at step208. In an embodiment of step 210, the first value group includescustomers having a first set of modeled lifetime values, and the secondvalue group includes customers having a second set of modeled lifetimevalues. The modeled lifetime values in the first set of modeled lifetimevalues are higher than modeled lifetime values in the second set ofmodeled lifetime values.

When step 210 classifies the identified customer into the first valuegroup, at step 212 the call management process routes the identifiedcustomer to a first call queue assignment. When step 210 classifies theidentified customer into the second value group, at step 214 the callmanagement process routes the identified customer to a second call queueassignment.

In various embodiments of steps 212 and 214, the first call queueassignment comprises a first queue position in a call queue, and thesecond call queue assignment comprises a second queue position in a callqueue. In one embodiment, the call queue is a hold list for callers onhold. In another embodiment, the call queue is a call back queue.

In another embodiment of steps 212 and 214, the first call queueassignment is a first call queue for connection to an agent from a firstpool of call center agents. The second call queue assignment is a secondcall queue for connection to an agent from a second pool of call centeragents.

In various embodiments, the agents to which customers are routed atsteps 212 and 214 are associated with a sponsoring organization thatsells or supplies products with the assistance of the call center. In anembodiment, the organization generates sales of one or more productthrough advertisements that give a phone number to potential orprospective customers, and the potential customers call into the callcenter using this phone number. In embodiments of step 210 in which thefirst value group includes customers having modeled lifetime values thatare higher than modeled lifetime values of customers in the second valuegroup, steps 212 and 214 prioritize call center resources to pursue saleof offered product(s) to the higher-value group of customers.

In certain embodiments of steps 210, 212, and 214, in which callsreceived from an identified customer are routed to one of a first callqueue and a second call queue, the routing process can permitadjustments to take into account matching of call center resources(agents) to customers at a given time, herein referred to as resourcematching adjustments. As seen in the schematic diagram of a customerqueue arrangement 300 at FIG. 3, first call queue 302 consists of aqueue of length M (last queue position 306), to be connected to agents310 in a first pool of agents who are authorized to present an offer topurchase the product. Second call queue 304 consists of a queue oflength N (last queue position 308), to be connected to agents 312 in asecond pool of agents who are not authorized to present an offer topurchase the product.

In various embodiments, resource matching adjustments can includingadjustments to the binary classification model that classifies customersinto first and second value groups. For example, if there are adisproportionately high number of agents in the first pool 310 comparedto the second pool 312, the threshold for the first value group can bereduced to permit routing of additional customers to the first pool ofagents. In another example, if there are a disproportionately low numberof agents in the first pool 310 compared to the second pool 312, thethreshold for the first value group can be raised to permit routing offewer customers to the first pool of agents.

In various embodiments, resource matching adjustments can includeadjustments to the first queue of customers and second queue ofcustomers. Such adjustments may take into account the total populationof customers awaiting connection to call center agents, i.e., thecustomers in both queues 302 and 304 at a given time. At certain timesthere may be an unusual distribution of customers between the first andsecond queues, e.g., a disproportionately high number of customers inthe first queue (M is unusually high relative to N) or adisproportionately low number of customers in the first queue (M isunusually low relative to N). To address these unusual distributions,one or more customer may be shifted from the first queue 302 to thesecond queue 304, or one or more customer may be shifted from the secondqueue 304 to the first queue 302.

Both types of resource matching adjustment, i.e., adjustment of theclassification model, and direct adjustment of the call queues, may becombined.

FIG. 4 shows a system architecture for a customer management system 400of a contact center, also herein called a call center, according to anillustrative embodiment. In the present disclosure, the call center issometimes called an inbound call center or inbound contact center,referring to its primary function of receiving inbound customer calls.However, it should be understood that communications of the inbound callcenter on occasion may include outbound calls, or call backs, inresponse to inbound customer calls. Customer management system 400includes an inbound queue management system 402, also called an inboundcall management system. Inbound queue management system 402 managesassignment of inbound telephone calls for response by agents based onpredicted value of the inbound telephone call. Inbound queue managementsystem 402 includes an analytical engine 404 containing a callevaluation sub-module 408, and a predictive modeling module 410including first and second regression models 412 and 416, and first andsecond tree-based models 414 and 418.

Inbound call management system 402 is interfaced with one or moreenterprise databases 420, which are internal databases of the inboundcontact center. Internal databases include customer database 422, whichtracks individuals who are customers of the sponsoring organization ofthe call center or other client enterprise. Other internal databasesinclude call history database 424 and account information database 426.In an embodiment, analytical engine 404 interacts with externalservices, applications, and databases, such as third party databases430, through one or more application programmable interfaces, an RSSfeed, or some other structured format, via communication network 435. Inthe embodiment of FIG. 4, inbound queue management system 402 retrievesdata from one or more third party databases 430, including a consumerdemographic database 432, and a directory service database 434.

Predictive modeling module 410 includes two or more models that modelbehaviors of customers such as likelihood that a caller will purchase aproduct offered by the call center, and likelihood that the caller willlapse in payments for a purchased product. The predictive modelingmodule analyzes each inbound customer call using data associated with acustomer identifier for the inbound caller. This customer identifier maybe obtained from various sources by the call evaluation sub-module 408,including enterprise customer data retrieved from customer database 422.Input data used in predictive modeling includes data retrieved fromcustomer database 422 and may include data from other internal databases420. Additionally, input data used in predictive modeling includes datafrom third party databases 430. This input data also may include dataderived from the retrieved data that has been transformed by analyticalengine 404 in order to facilitate predictive modeling, as describedherein.

In the system and method of the present disclosure, for each identifiedcustomer predictive modeling module 410 selects among two or morepredictive models by selecting a predictive model for which a set ofenterprise customer data for the identified customer has the highestexpected impact on likelihood of a respective business outcomeassociated with that predictive model. In an embodiment, each of theplurality of predictive models is configured to determine a businessoutcome signal representative of one of more of likelihood of acceptingan offer to purchase a product, and the selected predictive model is theone of the plurality of predictive models for which the set ofenterprise customer data has a highest importance in determining therespective business outcome signal. In an embodiment, analytical engine404 analyzes the set of enterprise customer data retrieved and selectseither a first predictive model that includes regression model #1 412and tree-based model #1 414 (collectively called predictive model #1),or a second predictive model that includes regression model #2 416 andtree-based model #2 418 (collectively called predictive model #2). Inother embodiments, predictive modeling module 410 can include more thantwo predictive models, and analytical engine 404 can select combinationsof component models (e.g., regression model and tree-based model) otherthan predetermined pairings of these component models. The selection ofpredictive models based upon consistency of the selected predictivemodel as a modeling target with a set of enterprise customer dataretrieved for an identified customer improves reliability of value-basedclassification of identified customer for call routing. In addition,predictive modeling module 410 may build stronger predictive modelsusing enterprise customer data of the sponsoring organization.

In an embodiment, predictive model #1 412 and 414 is configured todetermine a first business outcome signal representative of thelikelihood of a first business outcome, and predictive model #2 416 and418 is configured to determine a first business outcome signalrepresentative of the likelihood of a second business outcome. Invarious embodiments, each of the first business outcome and the secondbusiness outcome comprises one or more of accepting an offer to purchasea product, not lapsing in payments for a purchased product, andaccepting an offer to purchase a product and not lapsing in payments forthe purchased products. The analytical engine 404 retrieves a set ofenterprise customer data associated with an identified customer, andselects either predictive model #1 or predictive model #2 to generate avalue prediction signal for the identified customer. In an embodiment,analytical engine 404 selects the predictive model for which the set ofenterprise customer data has the higher importance in determining therespective business outcome signal.

In an embodiment, analytical engine 404 includes logic for selecting apredictive model from a plurality of predictive models for which a setof enterprise customer data retrieved for an identified customer has thehighest importance in determining the respective business outcome. In anembodiment, analytical engine 404 stores lookup tables each of thepredictive models within predictive modeling module 410 with tableentries for various enterprise customer data, wherein each table entryincludes an expected impact on business value. In an embodiment,analytical engine 404 applies business logic to forecasting expectedimpact on business value for given categories of enterprise customerdata, and for weighing two or more enterprise customer data ascomponents of overall importance in determining expected impact onlikelihood of the respective business outcome. In an embodiment,analytical engine 404 adjusts the weight of two or more enterprisecustomer data as components of overall importance in determiningexpected impact on likelihood of the respective business outcome,depending on circumstances of the call center management system 400. Forexample, in the context of a call center campaign focused on one or moretypes of customer (prospect, lead, purchaser, new business applicant),analytical engine 404 may place greater importance on customer eventdata and attributions data than on activity event data in determiningthe respective business outcome.

Predictive modeling module 410 generates a value prediction signalrepresentative of one or more of the following customer behaviors: (a)likelihood that the customer will accept an offer to purchase a product,(b) likelihood that the customer will lapse in payments for a purchasedproduct, and (c) likelihood that the customer will accept an offer topurchase the product and will not lapse in payments for the purchasedproduct. In certain embodiments, the predictive modeling module canpredict more than one of these customer behaviors. For example, thepredictive model may first determine the customer behavior (a)likelihood that the customer will accept an offer to purchase a product,followed by determining the customer behavior (b) likelihood that thecustomer will lapse in payments for a purchased product, in order todetermine a value prediction signal. Based on this value predictionsignal, the analytical module, in conjunction with the predictivemodeling module, classifies each customer call into one of two, or more,value groups. Depending on the value group determined for each customercall, analytical engine 404 directs routing of the customer call to callqueue routing module 450 to await connection to an agent of the callcenter. Call queue routing module 450 assigns each inbound caller to oneof two or more call assignments based on the value prediction signal forthat caller; in FIG. 4, two call queue assignments—first call queueassignment 460 and second call queue assignment 470—are shown.Value-based classification of inbound calls by inbound call managementsystem 402 represents a significant improvement over traditional methodsof routing callers, such as “round-robin” caller routing.

Inbound call management system 402 interfaces with an inbound telephonecall receiving system 440. In customer management system 400, inboundcall management system 402 and call receiving system 440 may beintegrated in a single computing platform. Alternatively, these systemsmay be based on separate computing platforms. In certain embodiments,the computing platform(s) are interfaced with computer-telephoneintegration (“CTI”) middleware. In an embodiment, inbound telephone callreceiving system 440 includes a telephony device that accepts inboundtelephone calls through a telephony interface 441, such as conventionalT1 or fiber interfaces. Inbound telephone call receiving system 440accepts inbound telephone calls through interface 441 and obtains callerinformation associated with the inbound calls, such as Automatic NumberIdentification (“ANI”) and Dialed Number Identification Service (“DNIS”)information 445. Reference may be had to the description of inboundtelephone call receiving system 140 (FIG. 1), for a description ofcorresponding components of inbound telephone call receiving system 440.These components include Automatic Number Identification (“ANI”) 441;Dialed Number Identification Service (“DNIS”) information 445; AutomaticCall Distributor (“ACD”) system 442; Voice Response Unit (“VRU”) system444; private branch exchange (“PBX”) switch 446; Voice over InternetProtocol (“VOIP”) server 448; and combinations of such devices.

An identified customer can be an inbound caller for which the customermanagement system 400 has obtained reliable identifying data, includingenterprise customer data retrieved from customer database 422. This datacan be used by inbound queue management system 402 to retrieve oridentify additional data associated with that customer. In anembodiment, an identified customer is a customer for which the system400 has reliably identified at least one customer group associated withthe customer in customer database 422, and at least two of name,address, and zip code. In embodiments in which the customer has beenassociated over time with two or more customer groups, customeridentifying data may include attributions (also herein calledattributions data) retrieved by customer database 422, as furtherdescribed below.

In an embodiment, inbound call management system 402 communicates with athird party directory service 434 such as the WhitePages® onlinedirectory service, as described above for inbound telephone callreceiving system 140. For an inbound caller that is a new prospect ofthe enterprise, this additional caller identification information can bestored in customer database 422 as profile data for that prospect.

Inbound telephone calls received through interface 441 are distributedto call queue(s) routing module 450 for response by agents operatingtelephony devices. In the inbound telephone call receiving system 540 ofcustomer management system 500 shown in FIG. 5, answering queues 550control routing of inbound callers on hold to live agents. Answeringqueues module 550 includes a first call queue (hold list) 554 and asecond call queue (hold list) 558. First call queue (hold list) 554routes calls on hold for response by one of the agents in a first pool560 of call center agents, while second call queue (hold list) 558routes calls on hold for response by one of the agents in a second pool570 of call center agents. In an embodiment, call center agents in agentpools 560 and 570 are groups of customer service representatives oragents deployed at workstations or agent devices communicatively coupledto customer management system 500.

In the inbound telephone call receiving system 640 of customermanagement system 600 shown in FIG. 6, call back queue routing module650 controls call backs to customers by agents of the call center. Callback queue routing module 650 includes a call back queue 654 forcontrolling call back to the customer by a live agent. Call back queuerouting module 650 assigns different customers to different call backqueue positions, such as first queue position 660 and second queueposition 670. Although FIGS. 5 and 6 depict embodiments of the presentinvention that order inbound telephone calls, alternative embodimentsapply inbound queue management to schedule other types of inboundinquiries, such as e-mail or instant message inquiries.

In an embodiment, agents are associated with a sponsoring organizationthat sells or supplies products with the assistance of the call center.In an embodiment, the organization generates sales of one or moreproduct through advertisements that give a phone number to prospectivecustomers, and the prospective customers call into the call center usingthis phone number. In an embodiment of the answering queues system shownin FIG. 5, the agents in first pool 560 are authorized to offer anadvertised product to a prospective customer (inbound caller), while theagents in second pool 570 are not authorized to offer the advertisedproduct. In the present disclosure, for an agent to be authorized tooffer a product to a prospective customer or lead means that the agentis authorized to pursue a sale of the product to the lead. In thisembodiment, placing an inbound caller in the first call queue 554 may beconsidered a priority queue assignment, while placing the inbound callerin the second call queue 558 may be considered a subordinate queueassignment.

In an embodiment, a sponsoring organization for customer managementsystem 400 is an insurance company or other financial services company,and the agents may include insurance agents. In some cases, an insuranceagent may be associated with only a single insurance provider (sometimesreferred to as a “captive” insurance agent). In other cases, an“independent” insurance agent may be associated with several differentinsurance providers. In an embodiment of the system 500, the agents inthe first pool 560 are licensed to sell insurance. In some cases, theproducers may be licensed to sell different types of insurance products,might have different areas of expertise, needs, etc. In someembodiments, agents in the first pool 560 are selected for performancemetrics related to sales. Agent sales performance may be measured byaggregate sales productivity metrics, as well as distributed performancemetrics such as sales metrics by product types, etc.

While the agents in second pool 570 are not authorized to offer theproduct(s) to the inbound caller (prospective customer or lead), theseagents perform an important role in lead nurturing. Forwarding aninbound inquiry to a live agent with little or no wait time, sometimesreferred to herein as a “warm transfer,” has been observed tosignificantly increase probability of a successful sale to that customerin a later interaction. In some embodiments, agents in the second pool570 are selected for skills related to agent-customer communications,which can be measured in indicators of customer satisfaction such asfeedback on customer experiences.

FIG. 7 is a flowchart of a call management process 700 for automaticallyrouting an inbound call from a customer to one of a plurality of queues(e.g., two queues) based on predicted value of the inbound telephonecall. Upon identifying the customer, the process retrieves customerdemographic data associated with a customer identifier for theidentified customer. A predictive model, including a logistic regressionmodel operating in conjunction with a tree-based model, determines avalue prediction signal for the identified customer. Based on the valueprediction signal determined, the predictive model classifies theidentified customer into one of a first value group and a second valuegroup. In the event the predictive model classifies the identifiedcustomer into the first value group, the call management system routesthe identified customer to a first call queue for connection to one of afirst pool of call center agents who are authorized to present the offerto purchase the product. In the event the predictive model classifiesthe identified customer into the second value group, the call managementsystem routes the identified customer to a second call queue forconnection to one of a second pool of call center agents who are notauthorized to present the offer to purchase the product.

The plurality of steps included in process 700 may be performed by oneor more computing devices or processors in the customer managementsystem of FIG. 4. In an embodiment, the plurality of steps included inprocess 700 may be performed by an inbound queue management system 402of a customer management system 400 of a call center, in operativecommunication with an inbound call receiving system 440 that receives acustomer call of the identified customer.

The call management process 700 is initiated at step 702 in response tothe customer management system 400 receiving an incoming customer callassociated with a customer identifier. Customers may dial into thesystem via a public switched telephone network or via intrasitetelephony. Additionally, inbound calls may be transmitted via theInternet using Web-enabled browsers, personal digital assistants orother network-enabled devices; via mobile cellular telephones (notshown), which may be Web-enabled, or connected via a mobile switchingcenter.

In an embodiment, the customer identifier includes two or more of nameof the identified customer, address of the identified customer, and zipcode of the identified customer. In an embodiment, the customermanagement system obtains a customer identifier for the receivedcustomer call from caller information associated with the inbound calls,such as Automatic Number Identification (“ANI”) and Dialed NumberIdentification Service (“DNIS”) information. In an embodiment, thecustomer management system obtains a customer identifier for thereceived customer call via an automated telegreeter of a Voice ResponseUnit (“VRU”) system. In an embodiment, the customer management systemobtains a customer identifier for the received customer call from athird party directory service.

In an embodiment of step 702, the call management process retrieves thethird party customer demographic data. In an embodiment, the third partycustomer demographic data is associated with the customer identifiercontained in a third party demographic database. In an embodiment, thecall management process builds a simplified data file from the thirdparty demographic data indexed to the customer identifier as input tofurther steps of the process.

A step 704, the call management process determine via a value predictionsignal via a predictive model that includes a logistic regression modeland a tree-based model. In various embodiments, the value predictionsignal includes one or more of a first signal representative of alikelihood that the identified customer will accept an offer to purchasea product, a second signal representative of a likelihood that theidentified customer will lapse in payments for a purchased product, anda third signal representative of a likelihood that the identifiedcustomer will accept an offer to purchase the product and will not lapsein payments for the purchased product. In various embodiments, the valueprediction signal is a buy-only signal, a lapse-only signal, abuy-don't-lapse signal, or a combination of these signals.

In various embodiments of step 704, the value prediction signaldetermines a likelihood that the identified customer will lapse inpayments for a purchased product by determining a likelihood that theidentified customer will fail to make a payment for the purchasedproduct during a predetermined time period following purchase of thepurchased product. In an embodiment, the predetermined time period waspreviously determined by modeling lifetime value over varying durationsof the time period following purchase of the purchased product.

In various embodiments of step 704, the regression model employs l₁regularization. In various embodiments, the logistic regression modelemploys l₂ regularization. In various embodiments, the tree-based modelis a random forests ensemble learning method for classification.

In an embodiment, the call management process retrieves customerdemographic data from a third party demographic database at step 702,and the logistic regression model of step 704 is trained on a full setof features of the third party demographic database.

At step 706, the call management process classifies the customer call(lead) into either a first value group or a second value group, based onthe value prediction signal determined at step 704. In an embodiment ofstep 706, the first value group includes customers having a first set ofmodeled lifetime values, and the second value group includes customershaving a second set of modeled lifetime values. The modeled lifetimevalues in the first set of modeled lifetime values are higher thanmodeled lifetime values in the second set of modeled lifetime values.

In the event step 706 classifies the identified customer into the firstvalue group, at step 708 the call management process routes theidentified customer to a first call queue for connection to one of afirst pool of call center agents who are authorized to present the offerto purchase the product. In the event step 706 classifies the identifiedcustomer into the second value group, at step 710 the call managementprocess routes the identified customer to a second call queue forconnection to one of a second pool of call center agents who are notauthorized to present the offer to purchase the product.

In various embodiments, the agents to which customers are routed atsteps 708, 710 are associated with a sponsoring organization that sellsor supplies products with the assistance of the call center. In anembodiment, the organization generates sales of one or more productthrough advertisements that give a phone number to potential orprospective customers, and the potential customers call into the callcenter using this phone number. In embodiments of step 706 in which thefirst value group includes leads having modeled lifetime values that arehigher than modeled lifetime values of leads in the second value group,steps 708 and 710 prioritize call center resources to pursue sale ofoffered product(s) to the higher-value group of leads.

In certain embodiments of steps 706, 708, and 710, the classification ofleads and routing of leads to the first call queue and second callqueue, can permit adjustments to take into account matching of callcenter resources (agents) to leads at a given time, herein referred toas resource matching adjustments. As seen in the schematic diagram of acustomer queue arrangement 300 at FIG. 3, first call queue 302 consistsof a queue of length M (last queue position 306), to be connected toagents 310 in a first pool of agents who are authorized to present anoffer to purchase the product. Second call queue 304 consists of a queueof length N (last queue position 308), to be connected to agents 312 ina second pool of agents who are not authorized to present an offer topurchase the product.

In various embodiments, resource matching adjustments can includingadjustments to the binary classification model that classifies leadsinto first and second value groups. For example, if there are adisproportionately high number of agents in the first pool 310 comparedto the second pool 312, the threshold for the first value group can bereduced to permit routing of additional leads to the first pool ofagents. In another example, if there are a disproportionately low numberof agents in the first pool 310 compared to the second pool 312, thethreshold for the first value group can be raised to permit routing offewer leads to the first pool of agents.

In various embodiments, resource matching adjustments can includeadjustments to the first queue of leads and second queue of leads. Suchadjustments may take into account the total population of leads awaitingconnection to call center agents, i.e., the leads in both queues 302 and304 at a given time. At certain times there may be an unusualdistribution of leads between the first and second queues, e.g., adisproportionately high number of leads in the first queue (M isunusually high relative to N) or a disproportionately low number ofleads in the first queue (M is unusually low relative to N). To addressthese unusual distributions, one or more customer may be shifted fromthe first queue 302 to the second queue 304, or one or more customer maybe shifted from the second queue 304 to the first queue 302.

Both types of resource matching adjustment, i.e., adjustment of thebinary classification model, and direct adjustment of the call queues,may be combined.

FIG. 8 shows a system architecture for a customer management system 800of a contact center, also herein called a call center, according to anillustrative embodiment. In the present disclosure, the call center issometimes called an inbound call center or inbound contact center,referring to its primary function of receiving inbound customer calls.The components and functions of customer management system 800 aregenerally similar to those of customer management system of FIG. 4, andreference should be had to discussion of the system 400 above for adescription of corresponding components and functions. Customermanagement system 800 includes an inbound queue management system 802,also called an inbound call management system. The inbound queuemanagement system 802 may be hosted on one or more computers (orservers), and the at least one computer may include or becommunicatively coupled to one or more databases. Inbound queuemanagement system 802 manages assignment of inbound telephone calls forresponse by agents in a queue position of a call queue for connection toa group of call center agents selected from a plurality of groups ofcall center agents. The group of call center agents is selected based onenterprise customer data for the caller (identified customer), while thequeue position is selected based on predicted value of the inboundtelephone call.

Predictive modeling module 810 models behaviors of customers such aslikelihood that a caller will purchase a product offered by the callcenter and likelihood that the caller will lapse in payments for apurchased product. The predictive modeling module analyzes each inboundcustomer call using data associated with a customer identifier for theinbound caller. This customer identifier may be obtained from varioussources by the call evaluation sub-module 808, including enterprisecustomer data retrieved from customer database 822. Input data used inpredictive modeling includes data retrieved from customer database 822and may include data from other internal databases 820. Additionally,input data used in predictive modeling includes data from third-partydatabases 830. This input data also may include data derived from theretrieved data that has been transformed by analytical engine 804 inorder to facilitate predictive modeling, as described herein.

Analytical engine 804 stores customer care interaction data representingattributes of one or more predetermined customer care interactionsimplemented by each group of agents within a plurality of groups 860 and870 of call center agents. Attributes of customer care interactions mayinclude, for example, general areas of responsibility, specific areas ofresponsibility, primary agent skills, secondary agent skills, targetcustomer groups, associated products, associated marketing campaigns,etc. As part of the process for routing a given inbound caller(identified customer) to call center agents, analytical engine 804compares the stored customer care data with a set of enterprise customerdata retrieved for that customer. Analytical engine 804 selects one ofthe plurality of groups of agents 860 and 870 based upon consistency ofthe predetermined customer care interaction for the selected group ofagents with the set of enterprise customer data retrieved for theidentified customer.

Predictive modeling module 810 generates a value prediction signalrepresentative of one or more of the following customer behaviors: (a)likelihood that the customer will accept an offer to purchase a product,(b) likelihood that the customer will lapse in payments for a purchasedproduct, and (c) likelihood that the customer will accept an offer topurchase the product and will not lapse in payments for the purchasedproduct. In certain embodiments, the predictive modeling module canpredict more than one of these customer behaviors. For example, thepredictive model may first determine the customer behavior (a)likelihood that the customer will accept an offer to purchase a product,followed by determining the customer behavior (b) likelihood that thecustomer will lapse in payments for a purchased product, in order todetermine a value prediction signal. Based on this value predictionsignal, the analytical module, in conjunction with the predictivemodeling module, classifies each customer call into one of two, or more,value groups.

Depending on the group of agents selected and the value group determinedfor each customer call, analytical engine 804 directs routing of thecustomer call to answering queues module 850 to await connection to anagent of the call center. Answering queues module 850 includes acomponent 854 that routes the inbound call to a call queue for a givengroup of agents from a plurality of groups of agents, and a component858 that assigns the inbound caller to a selected queue position in theselected call queue for live connection to an agent from the given groupof agents. In FIG. 8, two groups of call center agents with respectivecall queues—first agent group/call queue 860 and second agent group/callqueue 870—are shown. Routing inbound calls based on analysis of customercare interactions and of call value represents a significant improvementover traditional methods of routing callers, such as “round-robin”caller routing.

An identified customer can be an inbound caller for which the customermanagement system 800 has obtained reliable identifying data, includingenterprise customer data retrieved from customer database 822. This datacan be used by inbound queue management system 802 to retrieve oridentify additional data associated with that customer. In anembodiment, an identified customer is a customer for which the system800 has reliably identified at least one customer group associated withthe customer in customer database 822, and at least two of name,address, and zip code. In embodiments in which the customer has beenassociated over time with two or more customer groups, customeridentifying data may include attributions (also herein calledattributions data) retrieved by customer database 822, as furtherdescribed below.

In an embodiment, a sponsoring organization for customer managementsystem 800 is an insurance company or other financial services company,and the agents may include insurance agents. In some cases, an insuranceagent may be associated with only a single insurance provider (sometimesreferred to as a “captive” insurance agent). In other cases, an“independent” insurance agent may be associated with several differentinsurance providers. In an embodiment of the system 800, the agents inthe first group 860 are licensed to sell insurance. In some cases, theproducers may be licensed to sell different types of insurance products,might have different areas of expertise, needs, etc. In someembodiments, agents in the first group 860 are selected for performancemetrics related to sales. Agent sales performance may be measured byaggregate sales productivity metrics, as well as distributed performancemetrics such as sales metrics by product types, etc.

In an embodiment the agents in the second group 870 are not authorizedto offer the product(s) to the inbound caller (prospective customer orlead), but these agents are authorized to screen leads for prospectivecustomers. Such agents perform an important role in lead nurturing.Forwarding an inbound inquiry to a live agent with little or no waittime, sometimes referred to herein as a “warm transfer,” has beenobserved to significantly increase probability of a successful sale tothat customer in a later interaction. In some embodiments, agents in thesecond group 870 are selected for skills related to agent-customercommunications, which can be measured in indicators of customersatisfaction such as feedback on customer experiences.

FIG. 9 is a flowchart of a call management process 900 for automaticallyrouting an inbound call from an identified customer to a queue positionwithin a call queue to be connected to a one of a plurality of callcenter agents. The selected group of agents implements a predeterminedcustomer care interaction and is selected based upon consistency of thepredetermined customer care interaction with enterprise data pertainingto the identified customer. The inbound call's queue position within thecall queue for connection to the selected group of call center agents isbased on a predicted value of the inbound telephone call.

The method 900 retrieves from a customer database a set of enterprisecustomer data associated with an identified customer in a customer call.The customer database stores enterprise customer data associated withprospects, leads, and purchasers of an enterprise. In certainembodiments, the customer database also stores enterprise customer dataassociated with new business applicants of the customer enterprise. Themethod additionally retrieves customer demographic data associated withthe identified customer.

The method 900 selects a group of agents from a plurality of groups ofagents of the call center, wherein the selected group of agentsimplements a predetermined customer care interaction of the call center.The group of agents is selected based upon consistency of thepredetermined customer care interaction with the set of enterprisecustomer data for the identified customer.

A predictive model, including a logistic regression model operating inconjunction with a tree-based model, determines a value predictionsignal for the identified customer. Based on the value prediction signaldetermined, the predictive model classifies the identified customer intoone of a first value group, and a second value group. When thepredictive model classifies the identified customer into the first valuegroup, the call management system routes the customer call for theidentified customer to a first call queue position for connection to anagent from the selected group of agents. When the predictive modelclassifies the identified customer into the second value group, the callmanagement system routes the customer call for the identified customerto a second call queue position for connection to an agent from theselected group of agents.

The steps of process 900 may be performed by one or more computingdevices or processors in the customer management system of FIG. 8. In anembodiment, the plurality of steps included in process 900 may beperformed by an inbound queue management system 802 of a customermanagement system 800 of a call center, in operative communication withan inbound call-receiving system 840 that receives a customer call ofthe identified customer.

The call management process 900 is initiated at step 902 in response tothe customer management system 800 receiving an incoming customer call,by retrieving from a customer database enterprise customer dataassociated with an identified customer in the customer call. Thecustomer database stores enterprise customer data associated withprospects, leads, and purchasers of an enterprise. In variousembodiments, the enterprise customer data used in selecting the selectedpredictive model comprises one or more of customer event data, activityevent data, and attributions data. In certain embodiments, the customerdatabase also stores enterprise customer data associated with newbusiness applicants of the enterprise.

At step 904, the call management process retrieves customer demographicdata associated with the identified customer. In an embodiment, theprocess retrieves the customer demographic data from a third-partydatabase. In an embodiment, the third-party customer demographic data isassociated with identified customer by matching the data to one or moreenterprise customer identifiers stored by the customer database. In anembodiment, the call management process builds a simplified data filefrom the retrieved third-party demographic data as input to furthersteps of the process.

At step 906, the method selects a group of agents from a plurality ofgroups of agents of the call center, wherein the selected group ofagents implements a predetermined customer care interaction of the callcenter. The group of agents is selected based upon consistency of thepredetermined customer care interaction with the set of enterprisecustomer data retrieved from the customer database at step 902.

In an embodiment of step 906, a first predictive model from theplurality of predictive models is selected if the enterprise customerdata satisfies predetermined model selection criteria, and a secondpredictive model from the plurality of predictive models is selected ifthe enterprise customer data does not satisfy the predetermined modelselection criteria. In another embodiment of step 906, the selectedpredictive model is selected from three or more predictive models.

In various embodiments of step 906, the group of agents is selectedbased upon enterprise customer data for the inbound caller including oneor more of customer event data, activity event data, and attributionsdata. In an embodiment, the group of agents is selected to increase thelikelihood of achieving one or more predetermined customer careinteraction based upon consistency with the enterprise customer data forthat group of agents.

In an illustrative embodiment of step 906, the predetermined customercare interaction is customer development of leads, and the set ofenterprise customer data includes customer event data associated with aprospect. In another example, the predetermined customer careinteraction is cross-selling of products, and the set of enterprisecustomer data includes customer event data associated with a purchaser,and activity event data indicating likelihood of an additional purchase.In another example, the predetermined customer care interaction ispromoting a given product, and the set of enterprise customer dataincludes activity event data indicating customer interest in a marketingcampaign for the given product. In a further example, the predeterminedcustomer care interaction is closing product sales, and the set ofenterprise customer data includes one or more of customer event dataassociated with a prospect, customer event data associated with a newbusiness applicant, and attributions data associated with a prospect anda new business applicant.

At step 908, the call management process executes a predictive model todetermine a value prediction signal. In an embodiment, the predictivemodel applies a logistic regression model in conjunction with atree-based model to the retrieved customer demographic data. In anembodiment, the call management process executes the predictive model todetermine the value prediction signal in real time. By determining thevalue prediction signal in real time, the present method expeditiouslyroutes an incoming customer call to a call queue for connection to anagent from a selected group of call center agents.

In various embodiments of step 908, the value prediction signal includesone or more of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product.

In various embodiments of step 908, the value prediction signaldetermines a likelihood that the identified customer will lapse inpayments for a purchased product by determining a likelihood that theidentified customer will fail to make a payment for the purchasedproduct during a predetermined time period following purchase of thepurchased product. In an embodiment, the predetermined time period waspreviously determined by modeling lifetime value over varying durationsof the time period following purchase of the purchased product.

In various embodiments of step 908, the regression model employs l₁regularization. In various embodiments, the logistic regression modelemploys l₂ regularization. In various embodiments, the tree-based modelis a random forests ensemble learning method for classification.

At step 910, the call management process classifies the customer callfor the identified customer into either a first value group or a secondvalue group, based on the value prediction signal determined at step908. In an embodiment of step 910, the first value group includescustomers having a first set of modeled lifetime values, and the secondvalue group includes customers having a second set of modeled lifetimevalues. The modeled lifetime values in the first set of modeled lifetimevalues are higher than modeled lifetime values in the second set ofmodeled lifetime values.

When step 910 classifies the identified customer into the first valuegroup, at step 912 the call management process routes the customer callfor the identified customer to a first call queue position forconnection to an agent from the selected group of agents. When step 910classifies the identified customer into the second value group, at step914 the call management process routes the customer call for theidentified customer to a second call queue position for connection to anagent from the selected group of agents.

In an embodiment of steps 912, 914, the call queues are hold lists forcallers on hold. In another embodiment, the call queues are call-backqueues.

In embodiments of step 910 in which the first value group includescustomers having modeled lifetime values that are higher than modeledlifetime values of customers in the second value group, steps 912 and914 prioritize call center resources to the higher-value group ofcustomers.

As seen in the schematic diagram of a customer queue arrangement 300 atFIG. 3, first call queue 302 consists of a queue of length M (last queueposition 306), to be connected to agents 310 in a first group of agentswho are assigned to implement a first predetermined customer careinteraction. Second call queue 304 consists of a queue of length N (lastqueue position 308), to be connected to agents 312 in a second group ofagents who are assigned to implement a second predetermined customercare interaction

In an embodiment, steps 912 and 914 provide normal (FIFO) queuing forcallers within a given value group, and assign priority queue positionsto one value group and subordinate queue positions to another valuegroup. In various embodiments, the first queue position is locatedbehind other callers who were previously assigned a first queue positionin that call queue, and the second queue position is located behind thequeue position of other callers who were previously assigned a secondqueue position in that call queue. For example, in first call queue 302,if the most recent prior assignment of a first queue position was toqueue position M, the current assignment of a first queue position couldbe to a new queue position M+1 (not shown). If the most recent priorassignment of a second queue position was to queue position 3, thecurrent assignment of a second queue position could be to insert thecurrent inbound caller into queue position 4, and to adjust the priorcaller at queue position 4 to queue position 5, adjust the prior callerat queue position 5 to queue position 6, etc.

In an embodiment of steps 912, 914, the longest-waiting callers on holdin the subordinate value group may have a more advanced position in thequeue than the shortest-waiting callers on hold in the priority valuegroup. In an example of this arrangement, the first queue position isthe subordinate queue position and the second queue position is thepriority queue position. At step 914, the method assigns a second queueposition to a current inbound caller that is behind the most recentlyassigned second queue position, and is behind any callers previouslyassigned a first queue position who have waited more than apredetermined period of time for connection to an agent.

FIG. 10 is an architecture of a customer database 1000, representing anembodiment of the customer database 422 of FIG. 4 and of the customerdatabase 822 of FIG. 8. Customer database 1000 is an internal databaseof the sponsoring organization of the call center, or other enterprise.Customer database 1000 stores information on individual customers of theenterprise, associating these customers with one or more of the groupsProspects 1002, Leads 1004, New Business 1006 and Purchasers (Sales)1008. In the present disclosure, customer database records that identifyindividual customers of the enterprise, such as by name and phonenumber, are sometimes called “enterprise customer records.” Customerdatabase 1000 includes links between each customer group and each of theother groups. These links between customer groups are sometimes hereincalled attributions. There are unique keys 1012 between Purchasers(Sales) and each of the other data stores; a unique key 1014 betweenProspects 1002 and Leads 1004; a unique key 1016 between Prospects 1002and New Business 1006; and a unique key 1018 between Leads 1004 and NewBusiness 1006. In addition, customer database 1000 tracks event data forcustomer-related activities, such as promotional activities, customerprospecting activities, and call center CRM activities. Customerdatabase 1000 joins customer information across these four groups, aswell as attributions and events data, in order to better evaluatemarketing and call center activities, build stronger models, andgenerate useful reports.

In the embodiment of FIG. 4, enterprise customer data from customerdatabase 1000 is used in conjunction with customer data from third partydata sources 430, such as customer demographic data 432 and directoryservices data 434 based on phone number, to score incoming customers.This use case may also apply to the embodiments of FIG. 1 and FIG. 8.

Customer database 1000 employs attribution processes for trackingcustomers across events in customer acquisition and marketing. Theobjective of attribution is to track people across events: i.e.,prospects, leads, applications, and sales. Customer database 1000 usesexact matching of personal details in order to determine which prospectsmay have become leads, submitted new business applications, and/orbought products; and which leads may have submitted new businessapplications and/or bought products. In an embodiment, customer database1000 additionally employs matching algorithms for matching personaldetails with leads data retrieved from third party demographicdatabases.

The flow chart diagram of FIG. 11 shows attribution processes fortracking persons across events between the customer groups. FIG. 11shows four customer groups, herein sometimes called “customer event”data: prospects 1102, leads 1104, applications 1106, and sales 1108. Anindividual customer can follow several different paths. For example, thecustomer might be a prospect who goes straight to a sale; might gothrough the leads pipeline; might submit an application but never buythe product, etc. Events also can include activity events, such aspromotional activities, customer prospecting activities, and call centerCRM activities. In the present disclosure, customer database datatracking such activity events are sometimes called “activity event”data.

In an embodiment, events tracked by customer database 1100 include pairsof events consisting of an event that occurs earlier in time (alsoherein called prior event; e.g., event A) and an event that occurs laterin time (also herein called subsequent event; e.g., event B).Attributions, also called “attributions data” in the present disclosure,serve two primary functions. The first function is to trace allinstances of a prior event A to see where these instances ended up. Anexample of this function is: “Find all leads, applications, and salesthat resulted from prospecting activity on X date.” The second functionis to determine, for any record of a subsequent event B, which instanceof event A most likely caused event B. An example of this function is:“Which prospecting activities were responsible for TERM product salesthis month?”

Each arrow of FIG. 11 represents one of five attribution processes 1112,1114, 1116, 1118, and 1120. The illustrated embodiment does not includean attribution between applications and sales, because tracking betweenthem is very simple. In another embodiment, the attributions wouldinclude an attribution between applications and sales. Each arrow isnumbered (1, 2, 3, 4, or 5), representing the order in which theseattribution processes are run. In an embodiment, each attributionprocess carries out the following steps, in order: (1) Match recordsbetween event A and event B, where event B occurs later in time. Forexample, in the prospect to leads attribution 1112, prospect is event Aand leads is event B; (2) Filter matches based on a time limitdetermined by business rules; (3) Determine the best match, i.e., thesingle record from event A that most likely led to each record fromevent B; and (4) Load unique best matches to the attribution table,updating the historical table.

FIG. 12 is a schematic diagram of customer database event tables for thecustomer groups prospect, lead, new business, and sale; and ofattribution tables between events. Customer database event tables poolall prospects, leads, applications, and sales across the enterprise intofour standardized tables 1252, 1254, 1256, 1258. In an embodiment,prospect events data include, e.g., camp_cde (code of the marketingcampaign that targeted the prospect) and marketing_date (earliest knowndate for the prospect). In an embodiment, leads events data include,e.g., lead_creation_date (earliest known date for the lead) andsource_key (data that identifies the lead's corresponding prospect,where applicable). In an embodiment, new business events data include,e.g., role (role of the person in the record has on an insurance policy,such as owner, insured, or payer) and fyp (first year premium). In anembodiment, sale events data includes, e.g., policy_date (earliest knowndate for the policy) and vnb (value of new business).

In an embodiment of the system of FIG. 4, various data in customerdatabase 422 are also stored in other internal databases 420 of theenterprise, such as call history database 424 and account informationdatabase 426. The latter databases may act as source systems forcustomer database 422. Referring again to FIG. 12, customer databaserecords may have values in the columns source_table, source_id_column,and source_id, indicating how to access information in the sourcesystem.

Attribution creates attribution tables by applying rules to the customerdatabase event tables. The attribution tables 1264, 1268, 1272, 1276,and 1282 of FIG. 12 provide the basic data representing the relationshipbetween each pair of events 1252, 1254, 1256, 1258. In addition, thecustomer database 1200 can build overall tables that aggregate all therelationships between prospect, lead, new business, and sales. Forexample, if a prospect is attributed to a lead, which in turn isattributed to a sale, an overall table would represent theserelationships in a single row. In various embodiments, customer databasebuilds reports via overall tables that apply analytics to select datausing one or more of attribution tables 1264, 1268, 1272, 1276, and1282. In various embodiments, the analytics include criteria based onactivity events.

In an example, the customer database 1200 builds a report to answer thequestion: “What is the response rate for the Term to Perm campaign?” Thecustomer database selects data using themarketing.datamart_prospect_lead_attrib table 1264. The customerdatabase applies analytics to focus on the Term to Perm marketingcampaign, counting the number of leads generated from the totalprospects. In another example, the customer database 1200 builds areport to answer the question: “What is the conversion rate for theRetirement campaign?” The customer database selects data using themarketing.datamart_prospect_appl_attrib table 1268. The customerdatabase applies analytics to focus on the Retirement marketingcampaign, counting the percentage of applications generated from thetotal prospects.

The following discussion refers to customer management system 400 andits components, but also applies to customer management system 100 andcustomer management system 800. In an illustrative embodiment, customermanagement system 400 utilizes data from both internal and externalsources in pre-sale predictive modeling of sale of a financial product(insurance policy). The data includes internal data 420 of the callcenter that tracks historical information about leads, customers, andmarketing costs of the call center, including historical sales and lapseinformation. In an embodiment, these internal databases usermm_analytics schema in data warehouse software. In an embodiment,internal databases 420 use rmm_analytics schema to generate a table ofenterprise customer data. In another embodiment, internal databases 420use rmm_analytics schema to generate additional data tables, such as atable of historical lead and customer data, and a table of marketingcosts data.

In an embodiment, rmm_analytics schema includes sales and lapse data forcurrent and/or historical leads of the enterprise, which data is used inbuilding predictive models of the present disclosure. In an illustrativeembodiment, a paid_flag indicates policy payments and a related fieldshows the amount of each payment. In the present disclosure, these dataare called payment data. In an illustrative embodiment, either alapse_flag or surrendered_flag indicate that a policy has lapsed. In thepresent disclosure, these data are referred to as lapse data. In anembodiment, date fields are used for filtering data by date range. In anillustrative embodiment, information about leads, customers, andmarketing costs was used to model a pre-determined period of time ofpayments following the sale of the product that defines lapse. In anillustrative embodiment, for the purpose of pre-sale predictive modelingof sale of an insurance policy, this modeling resulted in defining lapseas failure of the customer to maintain a purchased policy in force forat least 18 months.

In building the predictive models of the present disclosure, modeldatasets may have populations in the hundreds of thousands or millionsof individuals. Model datasets may include training datasets and testingdatasets. Filtering techniques can be applied to eliminate false dataand for de-duplicating, reducing the number of records but significantlyimproving quality of model datasets. In an illustrative embodiment,date-filtered data such as payment data and lapse data within an olderdate range are used for building a training data set, and date-filtereddata within a more recent range are used for building a test data set.In an illustrative embodiment, predictive machine-learning models of thepresent disclosure are continually trained using updated payment data,lapse data, and customer demographic data.

In an embodiment, in building predictive models, rmm_analytics schemaare filtered based on the flow of historical leads through the inboundcall center routing process. In an embodiment, the data are filtered toselect only historical leads that were connected to a live agent; in thepresent disclosure this flow is sometimes called a “warm transfer.”Applicant has observed that building predictive models based on apopulation limited to warm transfers can improve performance of modelsfor predicting sales and lapse behaviors.

In the illustrative embodiment, data used in predictive modeling alsoinclude data retrieved from the customer demographic database 432 toobtain information about customers. In an embodiment, customerdemographic data includes individual level data on customers. In variousembodiments, as a prerequisite to using data in predictive modeling of agiven inbound caller (customer), analytical engine 404 associates thecustomer demographic data with a customer identifier for the customer.In an illustrative embodiment, customer demographic data used inmodeling of a customer requires an exact match of name and address.

In an embodiment, customer demographic data also includes data usingzip-level features of the system, which provides a coarserrepresentation in building the predictive model. Such zip-level featuresemploy variables that have resolution at the zip-level for eachindividual in the zip code. In an illustrative embodiment, zip-leveldata for individual income is associated with a median value of incomefor each individual in the zip code. Reasons for using zip-level data inpredictive modeling include, for example, lack of a statisticallysignificant difference in model performance as a function of any polymrmatch score threshold; simplicity of collecting only the name and zipcode in the telegreeter process; and privacy considerations as toindividual-level data.

In various embodiments embodiment, in predictive modeling of inboundcallers, inbound queue management system 402 uses a fast-lookup tool(e.g., polymr) that analyzes customer identifiers of inbound callers inreal time to retrieve customer data, such as customer demographic data,matched to the customer identifiers. In an embodiment, the polymrfast-lookup tool is a lightweight, extensible search engine, or API,implemented in the Python object-oriented programming language,https://www.python.org/. In various embodiments, the polymr toolperforms real time matching of data in the customer demographic database432 to a customer identifier for a given lead. In various embodiments,as a preliminary to using data in real-time predictive modeling ofinbound callers, inbound queue management system 402 indexes the data byapplying the search engine to customer identifiers in customer trainingdata, and stores this index as an internal enterprise database 420.

In an embodiment, inbound queue management system 402 labels each dataelement in Acxiom as continuous (including interval), binary, ordinal,or nominal (categorical). For use in a logistic regression model 414,variables that have lookup fields are converted to integers. Followingfeature transformation of the variables, the final view outputs eachvariable with human-readable names (if known), and a tag at the end ofthe variable name. Illustrative end tags for transformed variable namesinclude:

_binary: either 0 or 1

_ordinal_to_binary: either 0 or 1, where null values are mapped to 0

_flat_binary: mapped from a string field like “01001000” into multiplefields

_ordinal: as an integer, with null values left null

_interval: as an integer, with null values left null

_continuous: as an integer, with null values left null

_nominal: as an integer, with null values mapped to an additionalinteger

By applying the feature transformation rules described above, analyticalengine 404 builds a simplified input data file from data retrieved. Thissimplified input data file facilitates predictive modeling with a binarytarget.

Predictive modeling module 410 builds both a regression model 414 and atree-based model 418. In an embodiment, the predictive modeling module410 trains a logistic regression model 414 with l₁ regularization on thefull set of features of the Acxiom database. Use of logistic regressionfor classification problems provides performance advantages overstandard linear regression, because application of the logistic functionto the raw model score maps the output precisely from 0→1 whileproviding a smooth decision boundary. In an embodiment, the logisticregression model with l₁ regularization utilizes LASSO (Least AbsoluteShrinkage and Selection Operator), a regression analysis method thatperforms both variable selection and regularization to enhanceprediction accuracy and ease of interpretation of the resultingstatistical model.

l₁ regularization provides the benefit of simplifying the selection offeatures through the model training process by constraining featureswith lower correlation to have 0 weight. The general form for a linearmodel can be indicated as:ŷ=(w,x)=w _(o) +w ₁ x ₁ + . . . +w _(p) x _(p)for ŷ to be predicted from data points in the array x by learnedcoefficients w. The l₁ regularization is achieved by adding a term tothe cost function, as follows:

${\min\limits_{w}{\frac{1}{2n_{samples}}{{{Xw} - y}}_{2}^{2}}} + {a{w}_{1}}$with regularization weight α. Applicant observed in training a logisticregression model with l₁ regularization, that run time of trainingincreases rapidly with greater regularization parameters, with bestmodel performance at low values of the regularization parameter α. In anembodiment, the logistic regression model with l₁ regularization setsthe regularization parameter α using cross-validation, withbest-performing values typically around 0.005-0.01.

In another embodiment, regression model employs logistic regression withl₂ regularization, sometimes called ridge regression, according to theformula:

${\min\limits_{w}{\frac{1}{2n_{samples}}{{{Xw} - y}}_{2}^{2}}} + {a{w}_{2}}$

In the l₂ regularization model, as in the l₁ regularization model, theregularization weight α is set by cross validation. In an embodiment, alogistic regression model with l₂ regularization uses a backward featureselection procedure to select an optimal number of features. Thisfeature selection procedure is the RFECV method for recursive featureelimination in Scikit-learn, a software machine-learning library for thePython programming language, available athttps://github.com/scikit-learn/scikit-learn.

In various embodiments, both l₁ and l₂ regularization models fit aregularization hyperparameter using five folds for cross validation andsearching across the seven parameters: [0, 0.001, 0.005, 0.01, 0.1, 0.5,1]. In repeated iterations of model training, this range is restrictedaround previously successful settings.

In an embodiment, the tree-based model 418 is a random forests model.Random forests is a class of ensemble methods used for classificationproblems. Random forests models work by fitting an ensemble of decisiontree classifiers on sub samples of the data. Each tree only sees aportion of the data, drawing samples of equal size with replacement.Each tree can use only a limited number of features. By averaging theoutput of classification across the ensemble, the random forests modelcan limit over-fitting that might otherwise occur in a decision treemodel.

In an embodiment, the tree-based model 418 uses the random forests modelin Python's scikit-learn. In an illustrative embodiment, the tree-basedmodel 418 uses the following parameters in the scikit-learn randomforests model:

-   -   Maximum tree depth: 3 or ∞, set with max_depth.    -   Maximum number of features considered when looking for the best        split: 3→6, set with max_features.    -   Minimum number of samples required to split a node of the tree:        2→11, set with min_samples_split.    -   Minimum number of samples to be a leaf node: 1→11, set with        min_samples_leaf.    -   Number of trees in the forest: 100 or 200, set by n_estimators.    -   Whether to sample with replacement for the data seen by each        tree: true or false, set by bootstrap.    -   Function to measure quality of a split: Gini or Entropy        (information gain), set as criterion.

In an embodiment, for each customer a pre-sale predictive modelgenerates a value prediction signal indicative of potential value of asales transaction for that customer. The predictive model can providevarious types of value prediction signal including, for example: (a)buy-only signal, representative of the likelihood that the customer willaccept the offer to purchase the product; (b) lapse-only signalrepresentative of the likelihood that the customer will lapse inpayments for the purchased product; (c) buy-don't-lapse signalrepresentative of the likelihood that the customer will accept the offerto purchase the financial product and will not lapse in payments for thepurchased product; as well as predictive models providing combinationsof these signals.

Predictive models 410 effect a degree of feature selection. In variousembodiments, predictive models 410 identify high importance featuresthat have the most pronounced impact on predicted value. Different typesof model may identify different features as most important. For example,a model based upon a buy-only signal may identify different leadingfeatures than a model based upon a lapse-only signal.

TABLE 1 Features from l₁ buy-don't-lapse model Importance Feature−2.7125   expectant_parent_nominal −0.3126   recent_divorce_nominal_0−0.2634   credit_card_new_issue_nominal_0 −0.1438  gender_input_individual_nominal_0 0.1117 socially_influenced_ordinal0.0890 home_length_of_residence_interval −0.0757  likely_investors_nominal_0 −0.0667  vacation_travel_international_would_enjoy_ordinal_to_ binary 0.0637total_liquid_investible_assets_fin_ordinal −0.0632   new_mover_nominal_0−0.0518   single_parent_ordinal_to_binary −0.0517  vacation_travel_time_share_have_taken_ordinal_to_binary −0.0455  investments_real_estate_ordinal_to_binary 0.0438investments_stocks_bonds_ordinal_to_binary 0.0429obtain_life_insurance_along_with_loan_mortgage_installment_payments_ordinal

Table 1 shows the top 15 features from an l₁ buy-don't-lapse model. Themost important features are identified by the highest absolute value ofthe importance coefficient. The most important feature of this target isthe expectant_parent_nominal variable, where a 0 corresponds to notexpectant. Positive and negative signs of the importance coefficientindicate whether an increases, or a decrease, of the feature increaseslikelihood of the target. This data indicates that non-expectant parentsare less likely to buy and less likely to lapse.

In an embodiment, in building the predictive model 410, the call centerevaluates performance of prospective models, such as test models, forefficacy in predicting buying behavior and/or lapse behavior. In anembodiment, prospective models are tested for the area under the curve(AUC) of a receiver-operator curve (ROC). FIG. 13 is an example 1300 ofan ROC curve 1330. The receiver-operating characteristic (ROC) curveplots the true positive rate (Sensitivity) 1310 as a function of thefalse positive rate (100-Specificity) 1320 for different cut-off points.Each point on the ROC curve 1330 represents a sensitivity/specificitypair corresponding to a particular decision threshold. An ROC curve witha higher area under the curve (AUC) generally indicates ahigher-performing model. The ROC 1300 of FIG. 13 was obtained in testinga logistic regression model with l₁ regularization on the lapse-onlysignal, and has an area under the curve (AUC) 1340 of 0.574, indicatinga high-performing model.

FIG. 14 is another example of a receiver-operator curve (ROC) 1450,obtained by testing a logistic regression model with l₂ regularizationon the buy-only signal trained using all leads. (Sensitivity) 1460 as afunction of the false positive rate (100-Specificity) 1470 for differentcut-off points. Each point on the ROC curve 1480 represents asensitivity/specificity pair corresponding to a particular decisionthreshold. ROC 1450 has an area under the curve (AUC) 1490 of 0.531.

In an embodiment, prospective predictive models are tested forperformance by measuring lift across deciles. Lift is a measure of thedegree of improvement of a predictive model over analysis without amodel. For a binary classifier model, decile lift is applied to decilesof the target records ranked by predicted probability. FIG. 15 is agraph of lift across deciles of model scores 1500 for a logisticregression model with l₁ regularization on the lapse-only signal,trained on zip-level features. Percent of target values 1520 acrossdeciles 1510 show a significant impact of the model on lapse rate.

In an embodiment, prospective predictive models are tested forperformance by measuring improvements in buying behavior and/orreductions on lapse rate. In various embodiments, these measurements arecarried out with different levels of resource constraint of the callcenter, measured by call center agent resources in view of inbound callvolume. For example, a 70% resource constraint involves agent resourcesat a 70% level of resources in view of call volume relative to fullresources.

In illustrative embodiments, the predictive model incorporated alogistic regression model with l₁ regularization, for the lapse-onlytarget. In one illustrative embodiment, this model was trained on allcustomers with individual-level data. In another illustrativeembodiment, this model was trained on all customers with zip-level data.At a 70% resource constraint, the model with individual-level data wastested to provide an 11% reduction in lapse rate, while the model withzip-level data was tested to provide an 8% reduction in lapse rate. At a60% resource constraint, the model with individual-level data was testedto provide a 14% reduction in lapse rate, while the model with zip-leveldata was tested to provide an 11% reduction in lapse rate.

While various aspects and embodiments have been disclosed, other aspectsand embodiments are contemplated. The various aspects and embodimentsdisclosed are for purposes of illustration and are not intended to belimiting, with the true scope and spirit being indicated by thefollowing claims.

The foregoing method descriptions and the interface configuration areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc. are not intended to limit the orderof the steps; these words are simply used to guide the reader throughthe description of the methods. Although process flow diagrams maydescribe the operations as a sequential process, many of the operationscan be performed in parallel or concurrently. In addition, the order ofthe operations may be re-arranged. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination may correspond to a return ofthe function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedhere may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed here may be embodied in a processor-executable software modulewhich may reside on a computer-readable or processor-readable storagemedium. A non-transitory computer-readable or processor-readable mediaincludes both computer storage media and tangible storage media thatfacilitate transfer of a computer program from one place to another. Anon-transitory processor-readable storage media may be any availablemedia that may be accessed by a computer. By way of example, and notlimitation, such non-transitory processor-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other tangible storagemedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computeror processor. Disk and disc, as used here, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

What is claimed is:
 1. A processor-based method, comprising: in response to receiving a customer call from an identified customer at an inbound call receiving device: retrieving, by a processor, a set of customer data for the identified customer in the customer call; executing, by the processor, a predictive machine learning model configured to output a signal representative of likelihood of a business outcome by inputting the customer data for the identified customer to determine, for each of a plurality of customer records, the signal representative of the likelihood of the business outcome, the predictive machine learning model classifying the identified customer into a first value group or into a second value group based on the signal determined; and directing, by the processor, the inbound call receiving device, in the event the processor classifies the identified customer into the first value group, to route the identified customer to a first call queue assignment; in the event the processor classifies the identified customer into the second value group, to route the identified customer to a second call queue assignment.
 2. The processor based method according to claim 1, wherein the set of customer data for the identified customer is associated with one or more customer groups selected from the group consisting of prospects, leads, new business applicants, and purchasers.
 3. The processor based method according to claim 2, wherein the respective signal targets the one or more customer groups.
 4. The processor based method according to claim 1, wherein the customer data for the identified further comprises events data associating the identified customer with one or more customer acquisition or marketing events.
 5. The processor based method according to claim 4, wherein the events data is selected from the group consisting of a promotional activity, customer prospecting activity, and call center activity.
 6. The processor based method according to claim 1, further comprising the step of retrieving customer demographic data associated with the identified customer, wherein the predictive machine learning model is configured to output the signal upon inputting the customer data for the identified customer and the customer demographic data associated with the identified customer.
 7. The processor based method according to claim 4, wherein the customer demographic data for the identified customer comprises external third-party customer demographic data associated with a customer identifier for the identified customer.
 8. The processor based method according to claim 1, further comprising the step of selecting, by the processor, the predictive machine learning model from a plurality of predictive machine learning models, wherein each of the plurality of predictive machine learning models is configured to determine a respective signal representative of likelihood of a respective business outcome.
 9. The processor based method according to claim 6, wherein the selected predictive machine learning model is the one of the plurality of predictive machine learning models for which the set of enterprise customer data for the identified customer has a highest importance in determining the respective signal.
 10. The processor based method according to claim 1, wherein the first call queue assignment is a priority call queue assignment, and the second call queue assignment is a subordinate call queue assignment.
 11. The processor-based method according to claim 10, wherein the priority call queue assignment is a queue position in a hold list for callers on hold for live connection to an agent, and the subordinate call queue assignment is a queue position in a call-back queue.
 12. The processor based method according to claim 1, wherein the first value group comprises customers having a first set of modeled lifetime values, and the second value group comprises customers having a second set of modeled lifetime values, wherein modeled lifetime values in the first set of modeled lifetime values are higher than modeled lifetime values in the second set of modeled lifetime values.
 13. The processor based method according to claim 1, wherein the predictive machine learning model is comprises one of a logistic regression model with l₁ regularization or a logistic regression model with l₂ regularization.
 14. The processor based method according to claim 1, wherein the predictive machine learning model is a random forests ensemble learning method for classification.
 15. A system for managing customer calls within a call center, comprising: an inbound telephone call receiving device for receiving a customer call to the call center; and a non-transitory computer-readable medium storing a customer database and storing a computer program instructions, and a processor coupled to the non-transitory computer-readable medium and configured to execute the instructions to: upon receiving the customer call at the inbound telephone call receiving device from an identified customer, retrieve from the customer database a set of customer data for the identified customer in the customer call; output a signal representative of likelihood of a business outcome for the identified customer via applying a predictive machine learning model to the set of retrieved customer data to determine the signal representative of the likelihood of the business outcome for the identified customer, and classify the identified customer into one of a first value group and a second value group based on the signal determined; and direct the inbound telephone call receiving device: in the event the inbound queue management module classifies the identified customer into the first value group, to route the customer call of the identified customer to a first call queue assignment; in the event the inbound queue management module classifies the identified customer into the second value group, to route the customer call of the identified customer to a second call queue assignment.
 16. The system according to claim 15, wherein the processor is further configured to execute the instructions to: retrieve customer demographic data associated with the identified customer; and determine the signal representative of likelihood of a business outcome for the identified customer via applying the predictive machine learning model to the retrieved set of customer data and the customer demographic data associated with the identified customer.
 17. The system according to claim 15, wherein the processor is further configured to execute the instructions to: select one of a first predictive machine learning model or a second predictive machine learning model based upon the retrieved set of customer data for the identified customer; and determine the signal representative of likelihood of the business outcome for the identified customer via applying the selected predictive machine learning model to the retrieved set of customer data.
 18. The system according to claim 15, wherein the predictive machine learning model comprises a logistic regression model.
 19. The system according to claim 15, wherein the predictive machine learning model comprises a random forests ensemble learning method for classification. 